Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactive Privacy Preferences Management for Shared Spaces in Internet of Things

The balance between protecting users' privacy while providing cost-effective devices that are functional and usable is a key challenge in the burgeoning Internet of Things (IoT) industry. While in traditional desktop and mobile contexts the primary user interface is a screen, in IoT screens are rare or very small, which invalidate most of the traditional approaches. We examine how end-users interact with IoT products and how those products convey information back to the users, particularly 'what is going on' with regards to their data. We focus on understanding what the breadth of IoT, privacy, and ubiquitous computing literature tells us about how individuals with average technical expertise can be notified about the privacy-related information of the spaces they inhabit in an easily understandable way. In this survey, we present a review of the various methods available to notify the end-users while taking into consideration the factors that should be involved in the notification alerts within the physical domain. We identify five main factors: (1) data type, (2) data usage, (3) data storage, (4) data retention period, and (5) notification method. The survey also includes literature discussing individuals' reactions and their potentials to provide feedback about their privacy choices as a response to the received notification. The results of this survey highlight the most effective mechanisms for providing awareness of privacy and data-use-practices in the context of IoT in shared spaces.
EFFECTS OF DRY SEASON AND WET SEASON SUPPLEMENTATION ON FERTILITY OF Bos indicus CROSS COWS

Supplements providing principally inorganic N and/or P are often fed to breeder cows in the harsher environments of northern Australia to reduce breeder mortality and increase productivity (McCosker and Winks 1994). However, the responses to such supplements fed at various times of the year are not well understood. The present experiment examined the liveweight (LW) and fertility responses of Bos indicus cross breeders to dry lick supplements fed during the dry season (DS) and/or during the later part of the wet season (WS). First-calf (FCC) and second-calf (SCC) Bos indicus x Shorthorn females were grazed in 8 x 100 ha paddocks at Swan’s Lagoon from June 1994 until weaning in May 1995. The animals calved between late October 1994 and mid-January 1995, and were mated from December 1994 to April 1995. Four treatments were imposed in a replicated 2x2 factorial design consisting of dry lick supplements fed ad libitum during the DS from July to November 1994, and/or during the late WS from March to May 1995. DS supplements contained 32% cottonseed meal, 27% urea, 18% salt, 14% calcium phosphates and 9% ammonium sulphate. WS supplements contained 33% cottonseed meal, 30% salt, 21% calcium phosphates, 15% urea and 1% sulphur. Measurements were made each 6-8 weeks of LW and body condition score (CS). Pregnancy status and time of conception were determined by rectal palpation. Data were analysed for main effects and interaction effects by AOV.
Robust linear parameter-varying output-feedback control of permanent magnet synchronous motors

This paper investigates the design of a robust linear parameter-varying (LPV) controller for speed regulation of surface permanent magnet synchronous motors (SPMSMs). Motor dynamics is defined in the α–β stationary frame and a parameter-varying model formulation is provided to describe its nonlinear dynamics. A robust gain-scheduled LPV dynamic output-feedback controller is designed to guarantee the asymptotic stability and performance requirements, as well as, guarantee robustness against parameter perturbations and torque load disturbances. As a novel aspect in the SPMSM control, the real-time impact of temperature variation on the winding resistance and magnet flux during motor operation is explicitly considered in the LPV modeling as a scheduling parameter and the subsequent LPV control design compensating for demagnetization effects in the motor response. The controller synthesis conditions guaranteeing stability and robust performance to norm-bounded uncertainty in the system matrices are formulated in a convex linear matrix inequality (LMI) optimization framework. Finally, the validity of the proposed method is assessed in simulation studies. The closed-loop simulation studies demonstrate that the proposed LPV controller provides improved transient response with respect to settling time, overshoot, and disturbance rejection in tracking the velocity profile under the influence of parameter perturbation, temperature variations, and load disturbances.
The Youngest Planet to Have a Spin-Orbit Alignment Measurement AU Mic b

We report measurements of the sky-projected spin–orbit angle for AU Mic b, a Neptune-size planet orbiting a very young (∼20 Myr) nearby pre-main-sequence M-dwarf star, which also hosts a bright, edge-on, debris disk. The planet was recently discovered from preliminary analysis of radial-velocity observations and confirmed to be transiting its host star from photometric data from the NASA’s TESS mission. We obtained radial-velocity measurements of AU Mic over the course of two partially observable transits and one full transit of planet b from high-resolution spectroscopic observations made with the Minerva-Australis telescope array. Only a marginal detection of the Rossiter–McLaughlin effect signal was obtained from the radial velocities, in part due to AU Mic being an extremely active star and the lack of full transit coverage plus sufficient out-of-transit baseline. As such, a precise determination of the obliquity for AU Mic b is not possible in this study and we find a sky-projected spin–orbit angle of λ=47−54+26° . This result is consistent with both the planet’s orbit being aligned or highly misaligned with the spin axis of its host star. Our measurement independently agrees with, but is far less precise than observations carried out on other instruments around the same time that measure a low-obliquity orbit for the planet. AU Mic is the youngest exoplanetary system for which the projected spin–orbit angle has been measured, making it a key data point in the study of the formation and migration of exoplanets—particularly given that the system is also host to a bright debris disk.
Multi-path RNN for hierarchical modeling of long sequential data and its application to speaker stream separation

Recently, the source separation performance was greatly improved by time-domain audio source separation based on dual-path recurrent neural network (DPRNN). DPRNN is a simple but effective model for a long sequential data. While DPRNN is quite efficient in modeling a sequential data of the length of an utterance, i.e., about 5 to 10 second data, it is harder to apply it to longer sequences such as whole conversations consisting of multiple utterances. It is simply because, in such a case, the number of time steps consumed by its internal module called inter-chunk RNN becomes extremely large. To mitigate this problem, this paper proposes a multi-path RNN (MPRNN), a generalized version of DPRNN, that models the input data in a hierarchical manner. In the MPRNN framework, the input data is represented at several (>3) time-resolutions, each of which is modeled by a specific RNN sub-module. For example, the RNN sub-module that deals with the finest resolution may model temporal relationship only within a phoneme, while the RNN sub-module handling the most coarse resolution may capture only the relationship between utterances such as speaker information. We perform experiments using simulated dialogue-like mixtures and show that MPRNN has greater model capacity, and it outperforms the current state-of-the-art DPRNN framework especially in online processing scenarios.
A Cloud Computing Capability Model for Large-Scale Semantic Annotation

Semantic technologies are designed to facilitate context-awareness for web content, enabling machines to understand and process them. However, this has been faced with several challenges, such as disparate nature of existing solutions and lack of scalability in proportion to web scale. With a holistic perspective to web content semantic annotation, this paper focuses on leveraging cloud computing for these challenges. To achieve this, a set of requirements towards holistic semantic annotation on the web is defined and mapped with cloud computing mechanisms to facilitate them. Technical specification for the requirements is critically reviewed and examined against each of the cloud computing mechanisms, in relation to their technical functionalities. Hence, a mapping is established if the cloud computing mechanism’s functionalities proffer a solution for implementation of a requirement’s technical specification. The result is a cloud computing capability model for holistic semantic annotation which presents an approach towards delivering large-scale semantic annotation on the web via a cloud platform.
Antioxidant and anti-inflammatory activities of extracts of betel leaves (Piper betle) from solvents with different polarities

The influence of solvents with different polarities on the antioxidant and anti-inflammatory properties of betel leaf extracts (Piper betle) was investigated. The solvents used were water, ethanol, ethyl acetate and hexane. High performance liquid chromatography (HPLC) was used to determine the chemical profiles and concentrations of the active compounds, namely, hydroxychavicol (HC) and eugenol (EU). The antioxidant potential of the extracts was evaluated using two in vitro assays—xanthine/xanthine oxidase superoxide scavenging assay (SOD assay) and 1,2-diphenyl-2-picrylhydrazyl (DPPH) free radical scavenging assay (DPPH assay). The anti-inflammatory assays used were hyaluronidase (HYA), xanthine oxidase (XOD) and lipoxygenase (LOX) inhibition assays. The HPLC results revealed that HC and EU were detected in all types of extracts and the concentrations were highest in the water extract. The highest extraction yield was obtained using water. All the extracts were highly active in both antioxidant assays with water extract showing the strongest inhibition. The extracts also exhibited significant inhibition in XOD and LOX assays. The results indicated that the bioactivity of the extracts was related to HC and EU.
Notizen: The Mutagenicity of Dichloroacetaldehyde

Abstract Dichloroacetaldehyde, a presumed metabolite of the in secticides dichlorvos and tridilorphon, is mutagenic in the Salmonella/microsome test. Its mutagenic potency is higher than that of the established mutagen dichlorvos. It is pos sible that the bacterical mutagenicity test only or mainly detects the effect of methylation by dichlorvos.
Two Major Constraints on the Inner Radii of Accretion Disks

The Stefan–Boltzmann law yields a fundamental constraint on the geometry of inner accretion disks in black hole X-ray binaries. It follows from considering the irradiating flux and the effective temperature of the inner parts of the disk, which implies that a strong quasi-thermal component with the average energy higher than that of a blackbody at the effective temperature has to be present whenever relativistic Fe K fluorescence and reflection features are observed. The apparent absence of such quasi-thermal component with the color temperature of ∼1 keV in high-luminosity hard states is not compatible with a strongly irradiated disk extending close to the innermost stable circular orbit. Instead, the disk should be either truncated at a relatively large radius or irradiated by a corona at a large height, which would reduce the effective temperature and bring it to an agreement with the data. We also study constraints on disk/corona models following from comparing the disk densities fitted in literature using variable-density reflection codes with those calculated by us from the ionization parameter, the luminosity, and the disk inner radius. We find that the fitted densities are much higher/lower in the hard/soft state of binaries, implying significant problems with the used assumptions and methods.
Far-infrared laboratory spectroscopy of aminoacetonitrile and first interstellar detection of its vibrationally excited transitions

Context. Aminoacetonitrile, a molecule detected in the interstellar medium only toward the star-forming region Sagittarius B2 (Sgr B2), is considered an important prebiotic species; in particular, it is a possible precursor of the simplest amino acid glycine. To date, observations have been limited to ground state emission lines, whereas transitions from within vibrationally excited states remained undetected. Aims. We wanted to accurately determine the energies of the low-lying vibrational states of aminoacetonitrile, which are expected to be populated in Sgr B2(N1), the main hot core of Sgr B2(N). This step is fundamental in order to properly evaluate the vibration-rotation partition function of aminoacetonitrile as well as the line strengths of the rotational transitions of its vibrationally excited states. This is necessary to derive accurate column densities and secure the identification of these transitions in astronomical spectra. Methods. The far-infrared ro-vibrational spectrum of aminoacetonitrile has been recorded in absorption against a synchrotron source of continuum emission. Three bands, corresponding to the lowest vibrational modes of aminoacetonitrile, were observed in the frequency region below 500 cm−1. The combined analysis of ro-vibrational and pure rotational data allowed us to prepare new spectral line catalogs for all the states under investigation. We used the imaging spectral line survey ReMoCA performed with ALMA to search for vibrationally excited aminoacetonitrile toward Sgr B2(N1). The astronomical spectra were analyzed under the local thermodynamic equilibrium (LTE) approximation. Results. Almost 11 000 lines have been assigned during the analysis of the laboratory spectrum of aminoacetonitrile, thanks to which the vibrational energies of the v11 = 1, v18 = 1, and v17 = 1 states have been determined. The whole dataset, which includes high J and Ka transitions, is well reproduced within the experimental accuracy. Reliable spectral predictions of pure rotational lines can now be produced up to the THz region. On the basis of these spectroscopic predictions, we report the interstellar detection of aminoacetonitrile in its v11 = 1 and v18 = 1 vibrational states toward Sgr B2(N1) in addition to emission from its vibrational ground state. The intensities of the identified v11 = 1 and v18 = 1 lines are consistent with the detected v = 0 lines under LTE at a temperature of 200 K for an aminoacetonitrile column density of 1.1 × 1017 cm−2. Conclusions. This work shows the strong interplay between laboratory spectroscopy exploiting (sub)millimeter and synchrotron-based far-infrared techniques, and observational spectral surveys to detect complex organic molecules in space and quantify their abundances.
On Multivariate Singular Spectrum Analysis

We analyze a variant of multivariate singular spectrum analysis (mSSA), a widely used multivariate time series method, which we find to perform competitively with respect to the state-of-art neural network time series methods (LSTM, DeepAR). Its restriction for single time series, singular spectrum analysis (SSA), has been analyzed recently. Despite its popularity, theoretical understanding of mSSA is absent. Towards this, we introduce a natural spatio-temporal factor model to analyze mSSA. We establish the in-sample prediction error for imputation and forecasting under mSSA scales as $1/\sqrt{NT}$, for $N$ time series with $T$ observations per time series. In contrast, for SSA the error scales as $1/\sqrt{T}$ and for matrix factorization based time series methods, the error scales as ${1}/{\min(N, T)}$. We utilize an online learning framework to analyze the one-step-ahead prediction error of mSSA and establish it has a regret of ${1}/{(\sqrt{N}T^{0.04})}$ with respect to in-sample forecasting error. By applying mSSA on the square of the time series observations, we furnish an algorithm to estimate the time-varying variance of a time series and establish it has in-sample imputation / forecasting error scaling as $1/\sqrt{NT}$. To establish our results, we make three technical contributions. First, we establish that the "stacked" Page Matrix time series representation, the core data structure in mSSA, has an approximate low-rank structure for a large class of time series models used in practice under the spatio-temporal factor model. Second, we extend the theory of online convex optimization to address the variant when the constraints are time-varying. Third, we extend the analysis prediction error analysis of Principle Component Regression beyond recent work to when the covariate matrix is approximately low-rank.
Slice Sampling for General Completely Random Measures

Completely random measures provide a principled approach to creating flexible unsupervised models, where the number of latent features is infinite and the number of features that influence the data grows with the size of the data set. Due to the infinity the latent features, posterior inference requires either marginalization---resulting in dependence structures that prevent efficient computation via parallelization and conjugacy---or finite truncation, which arbitrarily limits the flexibility of the model. In this paper we present a novel Markov chain Monte Carlo algorithm for posterior inference that adaptively sets the truncation level using auxiliary slice variables, enabling efficient, parallelized computation without sacrificing flexibility. In contrast to past work that achieved this on a model-by-model basis, we provide a general recipe that is applicable to the broad class of completely random measure-based priors. The efficacy of the proposed algorithm is evaluated on several popular nonparametric models, demonstrating a higher effective sample size per second compared to algorithms using marginalization as well as a higher predictive performance compared to models employing fixed truncations.
A REVIEW ON REPRODUCTIVE TOXICITY OF ORGANOCHLORINE PESTICIDES

A significant increase in the incidence of male infertility has been described in the recent literature, raising questions about its causes. Pesticides such as organophosphates, organochlorine, phthalates, pyrethroids, carbamates and other substances from various industrial uses and residues are among the main endocrine disruptors that cause reproductive toxicity and male infertility. Organochlorines are a group of various synthetic chemicals that include polychlorinated biphenyls (PCBs), dibenzo-p-dioxiins and organochlorine pesticides. Human exposure to organochlorine substances may occur through inhalation of air, ingestion of food, water and skin absorption. Human exposure to organochlorines may occur not only during adulthood but also during prenatal and neonatal period. Many organochlorine pesticides are endocrine disrupting chemicals, meaning they have toxic effects on the hormonal systems. This review provides some specialized literature on work-related to organochlorine pesticides capable of causing male infertility.
Inverted pendulum driven by a horizontal random force: statistics of the never-falling trajectory and supersymmetry

We study stochastic dynamics of an inverted pendulum subject to a random force in the horizontal direction (Whitney's problem). Considered on the entire time axis, the problem admits a unique solution that always remains in the upper half plane. Assuming a white-noise driving, we develop a field-theoretical approach to statistical description of this never-falling trajectory based on the supersymmetric formalism of Parisi and Sourlas. The emerging mathematical structure is similar to that of the Fokker-Planck equation, which however is written for the ``square root'' of the probability distribution function. In the limit of strong driving, we obtain an exact analytical solution for the instantaneous joint distribution function of the pendulum's angle and its velocity.
Anti-tumor effect of pEgr-IFNγ gene-radiotherapy in B16 melanoma-bearing mice

AIM: To construct a pEgr-IFNγ plasmid and to investigate its expression properties of interferon-γ (INF-γ ) induced by irradiation and the effect of gene-radiotherapy on the growth of melanoma. METHODS: A recombined plasmid, pEgr-IFNγ , was constructed and transfected into B16 cell line with lipofectamine. The expression properties of pEgr-IFNγ were investigated by ELISA. Then, a B16 melanoma-bearing model was established in mice, and the plasmid was injected into the tumor tissue. The tumor received 20 Gy X-ray irradiation 36 h after injection, and IFN-γ expression was detected from the tumor tissue. A tumor growth curve at different time points was determined. RESULTS: The eukaryotic expression vector, pEgr-IFNγ , was successfully constructed and transfected into B16 cells. IFN-γ expression was significantly increased in transfected cells after X-ray irradiation in comparison with 0 Gy group (77.73-94.60 pg/mL, P < 0.05-0.001), and was significantly higher at 4 h and 6 h than that of control group after 2 Gy X-ray irradiation (78.90-90.00 pg/mL, P < 0.01-0.001). When the transfected cells were given 2 Gy irradiation 5 times at an interval of 24 h, IFN-γ expression decreased in a time-dependent manner. From d 3 to d 15 after IFNγ gene-radiotherapy, the tumor growth was significantly slower than that after irradiation or gene therapy alone. CONCLUSION: The anti-tumor effect of pEgr-IFNγ gene-radiotherapy is better than that of genetherapy or radiotherapy alone for melanoma. These results may establish an important experimental basis for gene-radiotherapy of cancer.
Direct evaluation of measurement uncertainties by feedback compensation of decoherence

It is difficult to evaluate the precision of quantum measurements because it is not possible to conduct a second reference measurement on the same physical system to compare the measurement outcome with a more accurate value of the measured quantity. Here, I show that a direct evaluation of measurement uncertainties is possible when the measurement outcomes are used to compensate the small amount of decoherence induced in a probe qubit by carefully controlled interactions with the system. Since the original uncertainty of the target observable causes fluctuating phase shifts in the probe qubit, any additional information obtained about the target observable can be used to compensate a part of the decoherence by applying a conditional phase shift to the reference qubit. The magnitude of this negative feedback corresponds to an estimate of the target observable, and the uncompensated decoherence defines the uncertainty of that estimate. The results of the analysis show that the uncertainties of the estimates are given by the uncertainties introduced by Ozawa in Phys. Rev. A 67, 042105 (2003) and the optimal estimates are given by the weak values associated with the different measurement outcomes. Feedback compensation of decoherence therefore demonstrates the empirical validity of definitions of errors and estimates that combine the initial information of the input state with the additional information provided by each measurement outcome.