Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination of Stellar Parameters through the Use of All Available Flux Data and Model Spectral Energy Distributions

OF DISSERTATION Determination of Stellar Parameters through the use of All Available Flux Data and Model Spectral Energy Distributions Basic stellar atmospheric parameters, such as e↵ective temperature, surface gravity, and metallicity plays a vital role in the characterization of various stellar populations in the Milky Way. The Stellar parameters can be measured by adopting one or more observational techniques, such as spectroscopy, photometry, interferometry, etc. Finding new and innovative ways to combine these observational data to derive reliable stellar parameters and to use them to characterize some of the stellar populations in our galaxy is the main goal of this thesis. Our initial work, based on the spectroscopic and photometric data available in literature, had the objective of calibrating the stellar parameters from a range of available flux observations from far-UV to far-IR. Much e↵ort has been made to estimate probability distributions of the stellar parameters using Bayesian inference, rather than point estimates. We applied these techniques to blue straggler stars (BSSs) in the galactic field, which are thought to be a product of mass transfer mechanism associated with binary stars. Using photometry available in SDSS and GALEX surveys we identified 85 stars with UV excess in their spectral energy distribution (SED) : indication of a hot white dwarf companion to BSS. To determine the parameter distributions (mass, temperature and age) of the WD companions, we developed algorithms that could fit binary model atmospheres to the observed SED. The WD mass distribution peaks at 0.4M , suggests the primary formation channel of field BSSs is Case-B mass transfer, i.e. when the donor star is in red giant phase of its evolution. Based on stellar evolutionary models, we estimate the lower limit of binary mass transfer e ciency ⇠ 0.5. Next, we have focused on the Canis Major overdensity (CMO), a substructure located at low galactic latitude in the Milky Way, where the interstellar reddening (E(B V )) due to dust is significantly high. In this study we estimated the reddening, metallicity distribution and kinematics of the CMO using a sample of red clump (RC) stars. The average E(B V ) (⇠ 0.19) is consistent with that measured from Schlegel maps (Schlegal et.al. 1998). The overall metallicity and kinematic distribution is in agreement with the previous estimates of the disk stars. But the measured mean alpha element abundance is relatively larger with respect to the expected value for disk stars.
A multi-objective robust optimization approach for engineering design under interval uncertainty

Purpose Engineering system design and optimization problems are usually multi-objective, constrained and have uncertainties in the inputs. These uncertainties might significantly degrade the overall performance of engineering systems and change the feasibility of the obtained solutions. In this paper, a multi-objective robust optimization approach based on Kriging metamodel (K-MORO) is proposed to obtain the robust Pareto set under the interval uncertainty. Design/methodology/approach In K-MORO, the nested optimization structure is reduced into a single loop optimization structure to ease the computational burden. Considering the interpolation uncertainty from the Kriging metamodel may affect the robustness of the Pareto optima, an objective switching and sequential updating strategy is introduced in K-MORO to determine (1) whether the robust analysis or the Kriging metamodel should be used to evaluate the robustness of design alternatives, and (2) which design alternatives are selected to improve the pre.
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library

CleverHans is a software library that provides standardized reference implementations of adversarial example construction techniques and adversarial training. The library may be used to develop more robust machine learning models and to provide standardized benchmarks of models' performance in the adversarial setting. Benchmarks constructed without a standardized implementation of adversarial example construction are not comparable to each other, because a good result may indicate a robust model or it may merely indicate a weak implementation of the adversarial example construction procedure. This technical report is structured as follows. Section 1 provides an overview of adversarial examples in machine learning and of the CleverHans software. Section 2 presents the core functionalities of the library: namely the attacks based on adversarial examples and defenses to improve the robustness of machine learning models to these attacks. Section 3 describes how to report benchmark results using the library. Section 4 describes the versioning system.
Ultra Wide Miniaturized Printed Antenna

ABSTRACT A wideband antenna covering 690 to 3,600 MHz is presented. It is built on an FR4 substrate with the dimension of 45 × 22.5 × 1.6 mm3. The antenna has one driven monopole broadside-coupled to a meandered line. To enhance the coupling, the monopole and the meandered line are on the opposite sides of the substrate. One 10-nH lumped inductor is also embedded in the meandered line. Further, two wider strips are added to the meandered line. The two wide strips do not generate new resonant modes, but they can effectively improve the match at the notched band. The notched band is between two separated bands usually found in the design of a mobile antenna. It makes the antenna not only cover the bandwidth in the current design of a mobile antenna, but also cover the potential application to the GPS and the WiMAX bands. Moreover, the size of the antenna is also small in an unfolded structure.
Fertility preservation in cancer patients: attenuation of chemotherapy-induced ovarian damage by oral administration of phytochemical carotenoids in an animal model

We investigated the protective effects of a phytochemical carotenoid-rich extract obtained from Crocus sativus L. , commonly known as saffron, against ovarian damage induced by cyclophosphamide (CPM). Mice received saffron extract (SE; 300 mg/kg) by oral administration for 15 days, followed by CPM treatment (100 mg/kg; SE+CPM group). The number of primordial and antral follicles decreased in CPM-mice when compared with control (p<0.001), whereas saffron administration prior to CPM partially prevented this loss. Protein expression of FOXO3a, a regulator of primordial follicle dormancy and adaptive response to oxidative stress, decreased in CPM-mice (p<0.05), whereas it was similar to control in the SE+CPM group (p=0.419). Levels of AGEs (Advanced-Glycation End-products), markers of oxidative stress and aging, were higher in CPM when compared to both control (p<0.05) and SE+CPM (p<0.05). Staining level for DNA double strand breaks increased in CPM ovaries (p<0.05), while it was similar to control in SE+CPM (p=0.194). These results provide evidence that oral administration of phytochemical carotenoids may counteract chemo-induced primordial follicle loss and oxidative damage. This approach may represent a pharmacological intervention to reduce the toxicity of alkylating agents so expanding options for fertility preservation in cancer patients.
Evaluating the climate effects of mid-1800s deforestation in New England, USA, using a Weather, Research, and Forecasting (WRF) Model Multi-Physics Ensemble

The New England region of the northeastern United States has a land use history characterized by forest clearing for agriculture and other uses during European colonization and subsequent reforestation following widespread farm abandonment. Despite these broad changes, the potential influence on local and regional climate has received relatively little attention. This study investigated wintertime (December through March) climate impacts of reforestation in New England using a high-resolution (4 km) multiphysics ensemble of the Weather Research and Forecasting Model. In general, the conversion from mid-1800s cropland/grassland to forest led to warming, but results were sensitive to physics parameterizations. The 2-m maximum temperature (T2max) was most sensitive to choice of land surface model, 2-m minimum temperature (T2min) was sensitive to radiation scheme, and all ensemble members simulated precipitation poorly. Reforestation experiments suggest that conversion of mid-1800s cropland/grassland to present-day forest warmed T2max 10.5 to 13K, with weaker warming during a warm, dry winter compared to a cold, snowy winter.Warmer T2max over forests was primarily the result of increased absorbed shortwave radiation and increased sensible heat flux compared to cropland/grassland. At night, T2min warmed 10.2 to 11.5K where deciduous broadleaf forest replaced cropland/grassland, a result of decreased ground heat flux. By contrast, T2min of evergreen needleleaf forest cooled –0.5 to –2.1K, primarily owing to increased ground heat flux and decreased sensible heat flux.
Some Fejer Type Inequalities for Harmonically-Convex Functions with Applications to Special Means

In this paper, the notion of harmonic symmetricity of functions is introduced. A new identity involving harmonically symmetric functions is established and some new Fejer type integral inequalities are presented for the class of harmonically convex functions. The results presented in this paper are better than those established in recent literature concerning harmonically convex functions. Applications of our results to special means of positive real numbers are given as well.
Multimodal fluorescence and photoacoustic microscopy in the frequency domain

In this paper a multimodal optical resolution microscope in frequency domain is introduced. Fluorescence and photoacoustic signals are generated simultaneously using an amplitude modulated diode laser. The resulting signals are recorded by a lock-in amplifier. By scanning the sample, two-dimensional plots of the fluorescence and photoacoustic signals can be generated. In this paper, the signal-to-noise ratios of amplitude modulated signals are compared to those expected for pulsed excitation under the assumption that the maximum permissible exposure limits are fulfilled. Furthermore, it is described how to determine the excited state life-times from the measured frequency responses of the fluorescence or photoacoustic signals, respectively.
Novel combination schemes of individual weighting factors sign subband adaptive filter algorithm

To improve the performance of the recently presented individual weighting factors sign subband adaptive filter (IWF‐SSAF) algorithm, its 2 combination algorithms using different step sizes are proposed. The first algorithm is to convexly combine the weight vectors of a large step‐size IWF‐SSAF filter and a small step‐size one; and the second algorithm is to obtain a time‐varying step size for the IWF‐SSAF by combining a large step size and a small one. The minimization of the sum of the l1‐norm of subband errors is used to indirectly update the mixing parameters in these 2 algorithms through a modified sigmoidal function. Moreover, in the first algorithm, to implement a smooth transition from the large step‐size IWF‐SSAF filter to the small step‐size one, the component filters receive a cyclic feedback of the combined weight vector. Both proposed algorithms have almost the same convergence performance, but the second algorithm saves computational cost. Simulation results in impulsive noise scenarios demonstrate the superiority of our proposed algorithms.
Solar Multi-Conjugate Adaptive Optics at the Dunn Solar Telescope

Solar observations are performed over an extended field of view and the isoplanatic patch over which conventional adaptive optics (AO) provides diffraction limited resolution is a severe limitation. The development of multi-conjugate adaptive optics (MCAO) for the next generation large aperture solar telescopes is thus a top priority. The Sun is an ideal object for the development of MCAO since solar structure provides ,,multiple guide stars” in any desired configuration. At the Dunn Solar Telescope (DST) we implemented a dedicated MCAO bench with the goal of developing well-characterized, operational MCAO. The MCAO system uses 2 deformable mirrors conjugated to the telescope entrance pupil and a layer in the upper atmosphere, respectively. DM2 can be placed at conjugates ranging from 2km to 10km altitude. We have successfully and stably locked the MCAO system on artificial objects (slides), for which turbulence screens are generated directly in front of the DMs, as well as solar structure. We present preliminary results and discuss future plans.
Lock-In Agreements in Venture Capital Backed UK IPOs

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Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success

Self-supervised learning (SSL) has emerged as a promising alternative to create supervisory signals to real-world problems, avoiding the extensive cost of manual labeling. SSL is particularly attractive for unsupervised tasks such as anomaly detection (AD), where labeled anomalies are rare or often nonexistent. A large catalog of augmentation functions has been used for SSL-based AD (SSAD) on image data, and recent works have reported that the type of augmentation has a significant impact on accuracy. Motivated by those, this work sets out to put image-based SSAD under a larger lens and investigate the role of data augmentation in SSAD. Through extensive experiments on 3 different detector models and across 420 AD tasks, we provide comprehensive numerical and visual evidences that the alignment between data augmentation and anomaly-generating mechanism is the key to the success of SSAD, and in the lack thereof, SSL may even impair accuracy. To the best of our knowledge, this is the first meta-analysis on the role of data augmentation in SSAD.
Safe Networked Robotics With Probabilistic Verification

Autonomous robots must utilize rich sensory data to make safe control decisions. To process this data, compute-constrained robots often require assistance from remote computation, or the cloud, that runs compute-intensive deep neural network perception or control models. However, this assistance comes at the cost of a time delay due to network latency, resulting in past observations being used in the cloud to compute the control commands for the present robot state. Such communication delays could potentially lead to the violation of essential safety properties, such as collision avoidance. This article develops methods to ensure the safety of robots operated over communication networks with stochastic latency. To do so, we use tools from formal verification to construct a shield, i.e., a run-time monitor, that provides a list of safe actions for any delayed sensory observation, given the expected and maximum network latency. Our shield is minimally intrusive and enables networked robots to satisfy key safety constraints, expressed as temporal logic specifications, with desired probability. We demonstrate our approach on a real F1/10th autonomous vehicle that navigates in indoor environments and transmits rich LiDAR sensory data over congested WiFi links.
Nowcasting Temporal Trends Using Indirect Surveys

Indirect surveys, in which respondents provide information about other people they know, have been proposed for estimating (nowcasting) the size of a hidden population where privacy is important or the hidden population is hard to reach. Examples include estimating casualties in an earthquake, conditions among female sex workers, and the prevalence of drug use and infectious diseases. The Network Scale-up Method (NSUM) is the classical approach to developing estimates from indirect surveys, but it was designed for one-shot surveys. Further, it requires certain assumptions and asking for or estimating the number of individuals in each respondent's network. In recent years, surveys have been increasingly deployed online and can collect data continuously (e.g., COVID-19 surveys on Facebook during much of the pandemic). Conventional NSUM can be applied to these scenarios by analyzing the data independently at each point in time, but this misses the opportunity of leveraging the temporal dimension. We propose to use the responses from indirect surveys collected over time and develop analytical tools (i) to prove that indirect surveys can provide better estimates for the trends of the hidden population over time, as compared to direct surveys and (ii) to identify appropriate temporal aggregations to improve the estimates. We demonstrate through extensive simulations that our approach outperforms traditional NSUM and direct surveying methods. We also empirically demonstrate the superiority of our approach on a real indirect survey dataset of COVID-19 cases.
Privacy-Utility Trade-offs in Neural Networks for Medical Population Graphs: Insights from Differential Privacy and Graph Structure

We initiate an empirical investigation into differentially private graph neural networks on population graphs from the medical domain by examining privacy-utility trade-offs at different privacy levels on both real-world and synthetic datasets and performing auditing through membership inference attacks. Our findings highlight the potential and the challenges of this specific DP application area. Moreover, we find evidence that the underlying graph structure constitutes a potential factor for larger performance gaps by showing a correlation between the degree of graph homophily and the accuracy of the trained model.
Detecting the sensitive spots of the African interurban transport network

Transport systems are vulnerable to events that create disruptions. This situation is particularly sensitive in parts of Africa with a low density of highways and an increasing level of violence. Here, we measure the risk of the African transport network based on two separate indices: the intensity of future events $\mu$ and the impact $\nu$ of one event on the flow that travels through the network. To estimate the intensity of future events happening in city $i$, we construct a self-exciting point process. To estimate the impact of an event, we consider a network of all highways in the continent and a modelled flow between any pair of cities. Based on both indicators, we construct the $\mu-\nu$ diagram and classify cities based on their combined impact and intensity. Results show that certain cities in the network have a high risk and increase the vulnerability of Africa's infrastructure. These cities have a high propensity for suffering subsequent violence against their civilians, and given their connectivity structure, they also substantially affect the overall regional functioning. Removing just ten edges would require rerouting 32% of trips according to our model. The top 100 edges where violence might happen account for 17% of the trips. We find that cities with the highest $\mu-\nu$ risk are those characterised by small and medium size and large degree, meaning they act as hubs. Vulnerable areas tend to be characterised by the presence of terrorist groups like Boko Haram in Nigeria.
Haldane phases and phase diagrams of the 
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We systematically study the effects of higher-order interactions on the S = 3/2, 1 orthogonal dimer chains using exact diagonalization and density matrix renormalization group. Due to frustration and higher spin, there are rich quantum phases, including three Haldane phases, two gapless phases and several magnetically ordered phases. To characterize these phases and their phase transitions, we study various physical quantities such as energy gap, energy level crossing, fidelity susceptibility, spin correlation, entanglement spectrum and central charge. According to our calculations, the biquadratic term can enhance the Haldane phase regions. In particular, we numerically identify that a Haldane phase in S = 3/2 case is adiabatically connected to the exact AKLT point when adding bicubic term. Our study on the orthogonal dimer model, which is a 1D version of Shastry-Sutherland model, provides insights into understanding the possible S = 3/2, 1 Haldane phases in quasi-1D and 2D frustrated magnetic materials.
A note on the incompleteness of Afshari&Leigh's system Clo

The system $\mathsf{Clo}$ is a cyclic, cut-free proof system for the modal $\mu$-calculus. It was introduced by Afshari&Leigh as an intermediate system in their intent to show the completeness of Kozen's axiomatisation for the modal $\mu$-calculus. We prove that $\mathsf{Clo}$ is incomplete by giving a valid sequent that is not provable in $\mathsf{Clo}$.
S-HR-VQVAE: Sequential Hierarchical Residual Learning Vector Quantized Variational Autoencoder for Video Prediction

We address the video prediction task by putting forth a novel model that combines (i) our recently proposed hierarchical residual vector quantized variational autoencoder (HR-VQVAE), and (ii) a novel spatiotemporal PixelCNN (ST-PixelCNN). We refer to this approach as a sequential hierarchical residual learning vector quantized variational autoencoder (S-HR-VQVAE). By leveraging the intrinsic capabilities of HR-VQVAE at modeling still images with a parsimonious representation, combined with the ST-PixelCNN's ability at handling spatiotemporal information, S-HR-VQVAE can better deal with chief challenges in video prediction. These include learning spatiotemporal information, handling high dimensional data, combating blurry prediction, and implicit modeling of physical characteristics. Extensive experimental results on the KTH Human Action and Moving-MNIST tasks demonstrate that our model compares favorably against top video prediction techniques both in quantitative and qualitative evaluations despite a much smaller model size. Finally, we boost S-HR-VQVAE by proposing a novel training method to jointly estimate the HR-VQVAE and ST-PixelCNN parameters.
Partitioning problems via random processes

There are a number of well-known problems and conjectures about partitioning graphs to satisfy local constraints. For example, the majority colouring conjecture of Kreutzer, Oum, Seymour, van der Zypen and Wood states that every directed graph has a 3-colouring such that for every vertex $v$, at most half of the out-neighbours of $v$ have the same colour as $v$. As another example, the internal partition conjecture, due to DeVos and to Ban and Linial, states that for every $d$, all but finitely many $d$-regular graphs have a partition into two nonempty parts such that for every vertex $v$, at least half of the neighbours of $v$ lie in the same part as $v$. We prove several results in this spirit: in particular, two of our results are that the majority colouring conjecture holds for Erd\H{o}s-R\'enyi random directed graphs (of any density), and that the internal partition conjecture holds if we permit a tiny number of"exceptional vertices". Our proofs involve a variety of techniques, including several different methods to analyse random recolouring processes. One highlight is a"personality-changing"scheme: we"forget"certain information based on the state of a Markov chain, giving us more independence to work with.