Miracle-dz/UniFAR-FTUs huggingface dataset:
CSI : audIology getting on your Patient ’ s good side

44 Welcome BAcK to an ongoing series that challenges the audiologist to identify a diagnosis for a case study based on a listing and explanation of the nonaudiology and audiology test battery. it is important to recognize that a hearing loss or a vestibular issue may be a manifestation of a systemic illness. Being part of the diagnostic and treatment “team” is a crucial role of the audiologist. securing the definitive diagnosis is rewarding for the audiologist and enhances patient hearing and balance health care and, often, quality of life.
Due date optimization in multi-objective scheduling of flexible job shop production

The manuscript presents the importance of integrating mathematical meth‐ ods for the determination of due date optimization parameter for maturity optimization in evolutionary computation (EC) methods in multi‐objective flexible job shop scheduling problem (FJSSP). The use of mathematical model‐ ling methods of due date optimization with slack (SLK) for low and total work content (TWK) for medium and high dimensional problems was presented with the integration into the multi‐objective heuristic Kalman algorithm (MOHKA). The multi‐objective optimization results of makespan, machine utilization and due date scheduling with the MOHKA algorithm were com‐ pared with two comparative multi‐objective algorithms. The high capability and dominance of the EC method results in scheduling jobs for FJSSP produc‐ tion was demonstrated by comparing the optimization results with the results of scheduling according to conventional priority rules. The obtained results of randomly generated datasets proved the high level of job scheduling im‐ portance with respect to the interdependence of the optimization parameters. The ability to apply the presented method to the real‐world environment was demonstrated by using a real‐world manufacturing system dataset applied in Simio simulation and scheduling software. The optimization results prove the importance of the due date optimization parameter in highly dynamic FJSSP when it comes to achieving low numbers of tardy jobs, short job tardiness and potentially lower tardy jobs costs in relation to short makespan of orders with highly utilized production capacities. The main findings prove that multi‐ objective optimization of FJSSP planning and scheduling, taking into account the optimization parameter due date, is the key to achieving a financially and timely sustainable production system that is competitive in the global market. © 2020 CPE, University of Maribor. All rights reserved.
Characterisation and in vitro antioxidant activity of probiotic Lactobacillus isolated from Inner Mongolia “ Hurood ” cheese

Probiotic bacteria possess tremendous capabilities that confer extensive health benefits by maintaining the gastrointestinal microbiota of the host organism. In the present work, we comprehensively investigated the probiotic characteristics and influence of selected Lactobacillus strains as antioxidative agents isolated from Inner Mongolian Cheese. For this purpose, the physiological properties, acid and bile tolerance, microbial adhesion to hydrocarbons, antimicrobial activity, and resistance of bacterial strains to six antibiotics were investigated. In vitro antioxidant analysis of selected LAB strains was performed, measuring scavenging activity against 2,2-diphenyl-1-picrylhydrazyl (DPPH) and hydroxyl radicals (OHˉ). Furthermore, 16S rRNA sequencing was used to identify four presumptive LAB strains. All four strains showed significant survival at pH 2.0 3.0 for 4 h, and with the addition of ox gall at 0.1 0.5% for 4 h. However, the cell viability of strain LP38 was log10 5.01 colony forming unit (CFU)/mL at pH 2.0, and log10 8.97 CFU/mL at pH 3.0, after 4 h of incubation. Strain LP38 showed more stability with 0.1% ox gall (log10 9.78 CFU/mL) than with 0.3 or 0.5% ox gall (w/v). Strains NS24 and LP38 showed better adherence and hydrophobicity to different solvents including xylene, vinyl acetate, and chloroform. Additionally, the antimicrobial results of strain LP38 against all five groups of pathogenic bacteria showed relatively higher inhibition than that by the other strains. Similarly, in vitro scavenging activity revealed that strain LP38 exhibited the highest hydroxyl (66.66% at 1010 CFU/mL) and 2,2-diphenyl-1-picrylhydrazyl (45.12%) free radicals. Overall, our findings provide new insights into the identification of Lactobacillus associated with potential probiotic properties.
Development of value-added butter through the addition of green tea ( Camellia sinensis L . ) extract

The present work highlights the potential production of value-added butter with enhanced nutritional and microbiological properties through the addition of 2 10% (w/w) green tea (Camellia sinensis L.) extract. The results revealed no significant difference in the moisture content (13 14% w/w) of all the butter samples. However, an increase in the amount of green tea extract resulted in a significant increase (p < 0.05) of the ash content (0.00 1.00%) and redness (a* value, 4.92 6.93), while both the lightness (L* value, 150.65 145.74) and yellowness (b* value, 54.45 50.30) of the butters significantly decreased (p < 0.05). Furthermore, the green tea butters (GTBs) exhibited significantly (p < 0.05) higher antioxidant properties in terms of total phenolic content (0.07 0.10 vs. 0.01 GAE% w/w db) and DPPH activity (7.27 13.94% vs. not detected) as compared to the control butter. After six weeks of storage, in relation to the control butter, the GTBs recorded significantly lower (p < 0.05) peroxide value (2.13 vs. 0.88 mEq/kg), total plate count (1.11 × 104 vs. 2.42 × 103 CFU/g), and yeast and mould count (2.02 × 103 vs. 6.05 × 102 CFU/g), but produced a significantly higher (p < 0.05) amount of acid value (0.56 vs. 1.36 mg KOH/g fat). The incorporation of up to 6% (w/w) green tea extract did not compromise the sensory acceptance of the GTBs. The overall result indicated that green tea extract can be used as a natural food additive, antioxidant, and preservative in butter.
Octaméthylène‐3,4;3',4' tétrathia‐2,2',5,5' fulvalène–tétracyano‐7,7,8,8 p‐quinodiméthane (OMTTF–TCNQ)

C14H1684.CI2H4N4, triclinic, PL a = 13.730(4), b = 6.832(2), c = 7.081 (2) A, a = 105.00(5), ~? = 78.48(5), y = 74.87(5) °, V = 593.3 A 3, Z = 1, F(000) = 268, M, = 516, d c = 1.445 Mg m -3, g(Cu Ka) = 3.72 mm-k The structure was solved by the heavy-atom method and refined to an R of 0.056 for the 1856 independent diffractometer- observed reflexions. OMTTF and TCNQ are alter- nately stacked along the (001) direction; the average perpendicular separation between the molecular planes is 3.53A. Bond lengths in TCNQ show that the constituent molecules in this compound are essentially neutral. The electrical conductivity of this compound, measured on single crystals, is cl ___ 10 -5 f~-I cm-l. Davidson & Simpson (1965). Ceux des atomes de soufre ont 6t6 corrig6s pour la dispersion anomale. La structure a 6t6 r6solue par la m6thode de l'atome lourd, l'analyse de la fonction de Patterson ayant permis de placer les atomes de soufre. L'affinement de la structure a 6t6 conduit de la faqon suivante: affinement avec agitation thermique isotrope (R = 0,14) puis anisotrope (R = 0,07) des positions des atomes S, N, O, C (les atomes de carbone des groupes m6thyl6nes pr6sentent un d6sordre de position; seuls les Tableau 1. Coordonn~es atomiques et facteurs d'agitation thermique Introduction. Le complexe OMTTF-TCNQ a 6t6 form6 par r6action d'oxydo-r6duction entre l'oxydant TCNQ, synth6tis6 selon la mh seule la m6thode de diffusion 6vaporation a permis de cristalliser ce complexe. S(l) S(2)
CANOPY

This article focuses on the new privacy challenges that arise in smart homes. Specifically, the article focuses on inferring the user’s activities—which may, in turn, lead to the user’s privacy—via inferences through device activities and network traffic analysis. We develop techniques that are based on a cryptographically secure token circulation in a ring network consisting of smart home devices to prevent inferences from device activities, via device workflow, i.e., inferences from a coordinated sequence of devices’ actuation. The solution hides the device activity and corresponding channel activities, thus preserving the individual’s activities. We also extend our solution to deal with a large number of devices and devices that produce large-sized data by implementing parallel rings. Our experiments also evaluate the performance in terms of communication overheads of the proposed approach and the obtained privacy.
Plasma Protein Adsorption and Platelet Adhesion on Heparin-Immobilized Polyurethane Films

Polyurethane surfaces were modified by covalent immobilization of different molecular weight heparins (fractionated Mw ~3000 Da, and unfractionated Mw ~17,000—19,000 Da) and examined by ESCA, AFM, and contact angle goniometer. The effect of these different surface-immobilized heparins on blood protein adsorption and on platelet adhesion, were examined after incubating the samples with platelet-poor and platelet-rich plasma. Protein adsorption kinetics was studied by electrophoresis and the platelets adhered on the surfaces were examined by SEM after incubation. The two heparin types clearly showed different behavior with respect to protein adsorption, especially in the early stages of blood plasma interaction. After a 5-min incubation in plasma, low molecular weight heparin-immobilized polyurethanes (PU-LMWH) showed three times less protein adsorption compared to unfractionated heparin-immobilized polyurethanes (PU-UFH). The total amount of adhered protein became more similar as the incubation time was extended. The morphology of adhered platelets on material surfaces demonstrated differences: PU-UFH had clusters with some pseudopodia extensions, while PU-LMWH surfaces had round-shaped platelets with little clustering. For contact times >15 min, the amount of adsorbed proteins and adhered platelets on the heparinized PU surfaces decreased.
GhostSR: Learning Ghost Features for Efficient Image Super-Resolution

Modern single image super-resolution (SISR) system based on convolutional neural networks (CNNs) achieves fancy performance while requires huge computational costs. The problem on feature redundancy is well studied in visual recognition task, but rarely discussed in SISR. Based on the observation that many features in SISR models are also similar to each other, we propose to use shift operation to generate the redundant features (i.e., ghost features). Compared with depth-wise convolution which is time-consuming on GPU-like devices, shift operation can bring a practical inference acceleration for CNNs on common hardwares. We analyze the benefits of shift operation on SISR task and make the shift orientation learnable based on Gumbel-Softmax trick. Besides, a clustering procedure is explored based on pre-trained models to identify the intrinsic filters for generating intrinsic features. The ghost features will be derived by moving these intrinsic features along a specific orientation. Finally, the complete output features are constructed by concatenating the intrinsic and ghost features together. Extensive experiments on several benchmark models and datasets demonstrate that both the non-compact and lightweight SISR models embedded with the proposed method can achieve a comparable performance to that of their baselines with a large reduction of parameters, FLOPs and GPU inference latency. For instance, we reduce the parameters by 46%, FLOPs by 46% and GPU inference latency by 42% of $\times2$ EDSR network with basically lossless performance.
Using space-VLBI to probe gravity around Sgr A*

Context. The Event Horizon Telescope (EHT) will soon provide the first high-resolution images of the Galactic Centre supermassive black hole candidate Sagittarius A* (Sgr A*), enabling us to probe gravity in the strong-field regime. In addition to studying the accretion process in extreme environments, the obtained data and reconstructed images could be used to investigate the underlying spacetime structure. In its current configuration, EHT is able to distinguish between a rotating Kerr black hole and a horizon-less object such as a boson star. Future developments can increase the ability of EHT to tell different spacetimes apart. Aims. We investigate the capability of an advanced EHT concept, including an orbiting space antenna, to image and distinguish different spacetimes around Sgr A*. Methods. We used general-relativistic magneto-hydrodynamical simulations of accreting compact objects (Kerr and dilaton black holes as well as boson stars) and computed their radiative signatures via general-relativistic radiative transfer. To facilitate a comparison with upcoming and future EHT observations, we produced realistic synthetic data including the source variability, diffractive, and refractive scattering while incorporating the observing array, including a space antenna. From the generated synthetic observations, we dynamically reconstructed black hole shadow images using regularised maximum entropy methods. We employed a genetic algorithm to optimise the orbit of the space antenna with respect to improved imaging capabilities and u − v-plane coverage of the combined array (ground array and space antenna) and developed a new method to probe the source variability in Fourier space. Results. The inclusion of an orbiting space antenna improves the capability of EHT to distinguish the spin of Kerr black holes and dilaton black holes based on reconstructed radio images and complex visibilities.
Internal Structure of Molecular Gas in a Main-sequence Galaxy With a UV Clump at z = 1.45

We present results of subarcsec Atacama Large Millimeter/submillimeter Array observations of CO(2–1) and CO(5–4) toward a massive main-sequence galaxy at z = 1.45 in the Subaru-XMM/Newton Deep Survey/UDS field, aiming at examining the internal distribution and properties of molecular gas in the galaxy. Our target galaxy consists of the bulge and disk, and has a UV clump in the Hubble Space Telescope images. The CO emission lines are clearly detected, and the CO(5–4)/CO(2–1) flux ratio (R 52) is ∼1, similar to that of the Milky Way. Assuming a metallicity-dependent CO-to-H2 conversion factor and a CO(2–1)/CO(1–0) flux ratio of 2 (the Milky Way value), the molecular gas mass and the gas-mass fraction (f gas = ratio of the molecular gas mass to the molecular gas mass + stellar mass) are estimated to be ∼1.5 × 1011 M ⊙ and ∼0.55, respectively. We find that R 52 peak coincides with the position of the UV clump and that its value is approximately twice higher than the galactic average. This result implies a high gas density and/or high temperature in the UV clump, which qualitatively agrees with a numerical simulation of a clumpy galaxy. The CO(2–1) distribution is well represented by a rotating-disk model, and its half-light radius is ∼2.3 kpc. Compared to the stellar distribution, the molecular gas is more concentrated in the central region of the galaxy. We also find that f gas decreases from ∼0.6 at the galactic center to ∼0.2 at three times the half-light radius, indicating that the molecular gas is distributed in the more central region of the galaxy than stars and seems to be associated with the bulge rather than with the stellar disk.
Assessing the Benefits of the Forest: the Views of Loggers, Forestry Students, and Citizens

This paper is an assessment of tangible and intangible benefits of the forest by three groups of people: loggers, university students of forestry and environmental management, citizens of Greece. The paper compares and contrasts the views of the above groups of people. According to the research, the loggers think that the most important functions of the forest are the production of wood products and the creation of opportunities for work. In contrast, forestry students think that the most important benefits of the forest are the production of oxygen and the ability to clean the atmosphere from pollutants. At a national level, citizens think that the most important benefits of the forest are the protection of wildlife and the production of oxygen.
Rate Region for Indirect Multiterminal Source Coding in Federated Learning

One of the main focus in federated learning (FL) is the communication efﬁciency since a large number of participating edge devices send their updates to the edge server at each round of the model training. Existing works reconstruct each model update from edge devices and implicitly assume that the local model updates are independent over edge device. In FL, however, the model update is an indirect multi-terminal source coding problem where each edge device cannot observe directly the source that is to be reconstructed at the decoder, but is rather provided only with a noisy version. The existing works do not leverage the redundancy in the information transmitted by different edges. This paper studies the rate region for the indirect multiterminal source coding problem in FL. The goal is to obtain the minimum achievable rate at a particular upper bound of gradient variance. We obtain the rate region for multiple edge devices in general case and derive an explicit formula of the sum-rate distortion function in the special case where gradient are identical over edge device and dimension. Finally, we analysis communication efﬁciency of convex Mini-batched SGD and non-convex Minibatched SGD based on the sum-rate distortion function, respectively.
SoK: Decentralized Finance (DeFi)

Decentralized Finance (DeFi), a blockchain powered peer-to-peer financial system, is mushrooming. Two years ago the total value locked in DeFi systems was approximately 700m USD, now, as of April 2022, it stands at around 150bn USD. The frenetic evolution of the ecosystem has created challenges in understanding the basic principles of these systems and their security risks. In this Systematization of Knowledge (SoK) we delineate the DeFi ecosystem along the following axes: its primitives, its operational protocol types and its security. We provide a distinction between technical security, which has a healthy literature, and economic security, which is largely unexplored, connecting the latter with new models and thereby synthesizing insights from computer science, economics and finance. Finally, we outline the open research challenges in the ecosystem across these security types.
COLLIDE-PRED: Prediction of On-Road Collision From Surveillance Videos

Predicting on-road abnormalities such as road accidents or traffic violations is a challenging task in traffic surveillance. If such predictions can be done in advance, many damages can be controlled. Here in our wok, we tried to formulate a solution for automated collision prediction in traffic surveillance videos with computer vision and deep networks. It involves object detection, tracking, trajectory estimation, and collision prediction. We propose an end-to-end collision prediction system, named as ‘Collide-Pred’, that intelligently integrates the information of past and future trajectories of moving objects to predict collisions in videos. It is a pipeline that starts with object detection, which is used for object tracking, and then trajectory prediction is performed which concludes by collision detection. The probable place of collision, and the objects those may cause the collision, both can be identified correctly with Collide-Pred. The proposed method is experimentally validated with a number of different videos and proves to be effective in identifying accident in advance.
Global optimisation of the mean first passage time for narrow capture problems in elliptic domains

Narrow escape and narrow capture problems which describe the average times required to stop the motion of a randomly travelling particle within a domain have applications in various areas of science. While for general domains, it is known how the escape time decreases with the increase of the trap sizes, for some specific 2D and 3D domains, higher-order asymptotic formulas have been established, providing the dependence of the escape time on the sizes and locations of the traps. Such results allow the use of global optimisation to seek trap arrangements that minimise average escape times. In a recent paper (Iyaniwura (2021) SIAM Rev.63(3), 525–555), an explicit size- and trap location-dependent expansion of the average mean first passage time (MFPT) in a 2D elliptic domain was derived. The goal of this work is to systematically seek global minima of MFPT for $1\leq N\leq 50$ traps in elliptic domains using global optimisation techniques and compare the corresponding putative optimal trap arrangements for different values of the domain eccentricity. Further, an asymptotic formula for the average MFPT in elliptic domains with N circular traps of arbitrary sizes is derived, and sample optimal configurations involving non-equal traps are computed.
Estimating Average Treatment Effects via Orthogonal Regularization

Decision-making often requires accurate estimation of treatment effects from observational data. This is challenging as outcomes of alternative decisions are not observed and have to be estimated. Previous methods estimate outcomes based on unconfoundedness but neglect any constraints that unconfoundedness imposes on the outcomes. In this paper, we propose a novel regularization framework for estimating average treatment effects that exploits unconfoundedness. To this end, we formalize unconfoundedness as an orthogonality constraint, which ensures that the outcomes are orthogonal to the treatment assignment. This orthogonality constraint is then included in the loss function via a regularization. Based on our regularization framework, we develop deep orthogonal networks for unconfounded treatments (DONUT), which learn outcomes that are orthogonal to the treatment assignment. Using a variety of benchmark datasets for estimating average treatment effects, we demonstrate that DONUT outperforms the state-of-the-art substantially.
Bayesian Bandwidths in Semiparametric Modelling for Nonnegative Orthant Data with Diagnostics

Multivariate nonnegative orthant data are real vectors bounded to the left by the null vector, and they can be continuous, discrete or mixed. We first review the recent relative variability indexes for multivariate nonnegative continuous and count distributions. As a prelude, the classification of two comparable distributions having the same mean vector is done through under-, equi- and over-variability with respect to the reference distribution. Multivariate associated kernel estimators are then reviewed with new proposals that can accommodate any nonnegative orthant dataset. We focus on bandwidth matrix selections by adaptive and local Bayesian methods for semicontinuous and counting supports, respectively. We finally introduce a flexible semiparametric approach for estimating all these distributions on nonnegative supports. The corresponding estimator is directed by a given parametric part, and a nonparametric part which is a weight function to be estimated through multivariate associated kernels. A diagnostic model is also discussed to make an appropriate choice between the parametric, semiparametric and nonparametric approaches. The retention of pure nonparametric means the inconvenience of parametric part used in the modelization. Multivariate real data examples in semicontinuous setup as reliability are gradually considered to illustrate the proposed approach. Concluding remarks are made for extension to other multiple functions.