Miracle-dz/UniFAR-FTUs huggingface dataset:
Complete genome sequence of chloroplast DNA (cpDNA) of Chlorella sorokiniana

Abstract The complete chloroplast genome sequence of Chlorella sorokiniana strain (SAG 111–8 k) is presented in this study. The genome consists of circular chromosomes of 109,811 bp, which encode a total of 109 genes, including 74 proteins, 3 rRNAs and 31 tRNAs. Moreover, introns are not detected and all genes are present in single copy. The overall AT contents of the C. sorokiniana cpDNA is 65.9%, the coding sequence is 59.1% and a large inverted repeat (IR) is not observed.
Rank-based persistence

Persistence has proved to be a valuable tool to analyze real world data robustly. Several approaches to persistence have been attempted over time, some topological in flavor, based on the vector space-valued homology functor, other combinatorial, based on arbitrary set-valued functors. To unify the study of topological and combinatorial persistence in a common categorical framework, we give axioms for a generalized rank function on objects in a target category, so that functors to that category induce persistence functions. We port the interleaving and bottleneck distances to this novel framework and generalize classical equalities and inequalities. Unlike sets and vector spaces, in many categories the rank of an object does not identify it up to isomorphism: to preserve information about the structure of persistence modules, we define colorable ranks, persistence diagrams and prove the equality between multicolored bottleneck distance and interleaving distance in semisimple Abelian categories. To illustrate our framework in practice, we give examples of multicolored persistent homology on filtered topological spaces with a group action and labeled point cloud data.
Weak lensing measurement of filamentary structure with the SDSS BOSS and Subaru Hyper Suprime-Cam data

We report the weak lensing measurement of filaments between Sloan Digital Sky Survey (SDSS) III/Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxy pairs at $z\sim0.55$, using the Subaru Hyper Suprime-Cam (HSC) first-year galaxy shape catalogue. Despite of the small overlap of $140$ deg$^2$ between these surveys we detect the filament lensing signal at 3.9$\sigma$ significance, which is the highest signal-to-noise lensing measurement of filaments between galaxy-scale halos at this redshift range. We derive a theoretical prediction and covariance using mock catalogues based on full-sky ray-tracing simulations. We find that the intrinsic scatter of filament properties and the fluctuations in large scale structure along the line-of-sight are the primary component of the covariance and the intrinsic shape noise from source galaxies no longer limits our lensing measurement. This fact demonstrates the statistical power of the HSC survey due to its deep observations and high number density of source galaxies. Our result is consistent with the theoretical prediction and supports the "thick" filament model. As the HSC survey area increases, we will be able to study detailed filament properties such as the dark matter distributions and redshift evolution of filaments.
2014 International Conference on Lightning Protection ( ICLP ) , Shanghai , China Lightning Induced Overvoltages on Overhead Lines Shielded by Nearby Buildings

In urban areas the presence of buildings is expected to reduce the amplitude of lightning induced voltages on overhead lines with respect to the case of open terrain. This paper presents a method for estimating such a reduction effect. For this purpose weighting functions are applied to the electrostatic, induction and radiation terms of the expressions adopted for the lightning electromagnetic field calculation assuming open terrain. The parameters of the weighting functions are identified by means of a least square fitting procedure using a finite element method model as benchmark for the field calculation. These functions are shown to be rather independent of the lightning return stroke current waveform and of the distance between the line and the stroke location. Although this paper presents only results obtained for the case of a single line, the method is conceived in order to be applicable for the lightning performance assessment of more complex power distribution networks. Keywords—finite element method, lightning-induced voltage, shielding effect of nearby buildings.
Uniqueness of Medical Data Mining: How the new technologies and data they generate are transforming medicine

The paper describes how the new technologies and data they generate are transforming medicine. It stresses the uniqueness of heterogeneous medical data and the ways of dealing with them. It lists different sources that generate big medical data, their security, legal and ethical issues, as well as machine learning/AI methods of dealing with them. A unique feature of the paper is use of case studies to illustrate how the new technologies influence medical practice.
Survival Function Matching for Calibrated Time-to-Event Predictions

Models for predicting the time of a future event are crucial for risk assessment, across a diverse range of applications. Existing time-to-event (survival) models have focused primarily on preserving pairwise ordering of estimated event times, or relative risk. Model calibration is relatively under explored, despite its critical importance in time-to-event applications. We present a survival function estimator for probabilistic predictions in time-to-event models, based on a neural network model for draws from the distribution of event times, without explicit assumptions on the form of the distribution. This is done like in adversarial learning, but we achieve learning without a discriminator or adversarial objective. The proposed estimator can be used in practice as a means of estimating and comparing conditional survival distributions, while accounting for the predictive uncertainty of probabilistic models. Extensive experiments show that the proposed model outperforms existing approaches, trained both with and without adversarial learning, in terms of both calibration and concentration of time-to-event distributions.
Comparison of microbial community structures in soils with woody organic amendments and soils with traditional local organic amendments in Ningxia of Northern China

Background Addition of organic amendments has been commonly adopted as a means to restore degraded soils globally. More recently, the use of woody organic amendments has been recognized as a viable method of capturing and retaining water and restoring degraded and desertified soil, especially in semi-arid regions. However, the impacts of woody amendments on soil microbial community structure, versus other traditional organic supplements is less understood. Methods Three locally available natural organic materials of different qualities, i.e., cow manure (CM), corn straw (CS), and chipped poplar branches (PB) were selected as treatments in Ningxia, Northern China and compared with control soils. Four microcosms served as replicates for each treatment. All treatments contained desertified soil; treatments with amendments were mixed with 3% (w/w) of one of the above organic materials. After 7 and 15 months from the start of the experiment, soil samples were analyzed for chemical and physical properties, along with biological properties, which included microbial α-diversity, community structure, and relative abundance of microbial phyla. Results Both bacterial and fungal α-diversity indices were weakly affected by amendments throughout the experimental period. All amendments yielded different microbial community compositions than the Control soils. The microbial community composition in the CS and PB treatments also were different from the CM treatment. After 15 months of the experiment, CS and PB exhibited similar microbial community composition, which was consistent with their similar soil physical and chemical properties. Moreover, CS and PB also appeared to exert similar effects on the abundance of some microbial taxa, and both of these treatments yield different abundances of microbial taxa than the CM treatment. Conclusion New local organic amendment with PB tended to affect the microbial community in a similar way to the traditional local organic amendment with CS, but different from the most traditional local organic amendment with CM in Ningxia, Northern China. Moreover, the high C/N-sensitive, and lignin and cellulose decompose-related microbial phyla increased in CS and PB have benefits in decomposing those incorporated organic materials and improving soil properties. Therefore, we recommend that PB should also be considered as a viable soil organic amendment for future not in Ningxia, but also in other places.
Correlation of the rate of Type Ia supernovae with the parent galaxy properties: Light and shadows

Context. The identification of the progenitors of Type Ia supernovae (SNIa) is extremely important in several astrophysical contexts, ranging from stellar evolution in close binary systems to evaluating cosmological parameters. Determining the distribution of the delay times (DTD) of SNIa progenitors can shed light on their nature. The DTD can be constrained by analysing the correlation between the SNIa rate and those properties of the parent galaxy which trace the average age of their stellar populations. Aims. We investigate the diagnostic capabilities of this correlation by examining its systematics with the various parameters at play: simple stellar population models, the adopted description for the star formation history (SFH) in galaxies, and the way in which the masses of the galaxies are evaluated. Methods. We computed models for the diagnostic correlations for a variety of input ingredients and for a few astrophysically motivated DTD laws appropriate for a wide range of possibilities for the SNIa progenitors. The models are compared to the results of three independent observational surveys. Results. The scaling of the SNIa rate with the properties of the parent galaxy is sensitive to all input ingredients mentioned above. This is a severe limitation on the possibility to discriminate alternative DTDs. In addition, current surveys show some discrepancies for the reddest and bluest galaxies, likely because of limited statistics and the inhomogeneity of the observations. For galaxies with intermediate colours the rates are in agreement, leading to a robust determination of the productivity of SNIa from stellar populations of ≃0.8 events per 1000 M⊙. Conclusions. Large stastistics of SNIa events along with accurate measurements of the SFH in the galaxies are required to derive firm constraints on the DTD. The LSST will achieve these results by providing a homogeneous, unbiased, and vast database on both SNIa and galaxies.
LapTool-Net: A Contextual Detector of Surgical Tools in Laparoscopic Videos Based on Recurrent Convolutional Neural Networks

We propose a new multilabel classifier, called LapTool-Net to detect the presence of surgical tools in each frame of a laparoscopic video. The novelty of LapTool-Net is the exploitation of the correlation among the usage of different tools and, the tools and tasks - namely, the context of the tools' usage. Towards this goal, the pattern in the co-occurrence of the tools is utilized for designing a decision policy for a multilabel classifier based on a Recurrent Convolutional Neural Network (RCNN) architecture to simultaneously extract the spatio-temporal features. In contrast to the previous multilabel classification methods, the RCNN and the decision model are trained in an end-to-end manner using a multitask learning scheme. To overcome the high imbalance and avoid overfitting caused by the lack of variety in the training data, a high down-sampling rate is chosen based on the more frequent combinations. Furthermore, at the post-processing step, the prediction for all the frames of a video are corrected by designing a bi-directional RNN to model the long-term task's order. LapTool-net was trained using a publicly available dataset of laparoscopic cholecystectomy. The results show LapTool-Net outperforms existing methods significantly, even while using fewer training samples and a shallower architecture.
Fine-grained Optimization of Deep Neural Networks

In recent studies, several asymptotic upper bounds on generalization errors on deep neural networks (DNNs) are theoretically derived. These bounds are functions of several norms of weights of the DNNs, such as the Frobenius and spectral norms, and they are computed for weights grouped according to either input and output channels of the DNNs. In this work, we conjecture that if we can impose multiple constraints on weights of DNNs to upper bound the norms of the weights, and train the DNNs with these weights, then we can attain empirical generalization errors closer to the derived theoretical bounds, and improve accuracy of the DNNs. To this end, we pose two problems. First, we aim to obtain weights whose different norms are all upper bounded by a constant number, e.g. 1.0. To achieve these bounds, we propose a two-stage renormalization procedure; (i) normalization of weights according to different norms used in the bounds, and (ii) reparameterization of the normalized weights to set a constant and finite upper bound of their norms. In the second problem, we consider training DNNs with these renormalized weights. To this end, we first propose a strategy to construct joint spaces (manifolds) of weights according to different constraints in DNNs. Next, we propose a fine-grained SGD algorithm (FG-SGD) for optimization on the weight manifolds to train DNNs with assurance of convergence to minima. Experimental results show that image classification accuracy of baseline DNNs can be boosted using FG-SGD on collections of manifolds identified by multiple constraints.
Alfvén Wave-driven Wind from RGB and AGB Stars

We develop a magnetohydrodynamical model of Alfvén wave-driven wind in open magnetic flux tubes piercing the stellar surface of red giant branch (RGB) and asymptotic giant branch (AGB) stars, and investigate the physical properties of the winds. The model simulations are carried out along the evolutionary tracks of stars with initial mass in the range of 1.5–3.0 M⊙ and initial metallicity Zini = 0.02. Setting the surface magnetic field strength to 1 G, we find that the wind during the evolution of the star can be classified into the following four types: the first is wind with velocity higher than 80 km s−1 in the RGB and early AGB (E-AGB) phases, the second is wind with outflow velocity less than 10 km s−1 seen around the tip of the RGB or in the E-AGB phase, the third is the unstable wind in the E-AGB and thermally pulsing AGB (TP-AGB) phases, and the fourth is the stable massive and slow wind with mass-loss rate higher than 10−7 M⊙ yr−1 and outflow velocity lower than 20 km s−1 in the TP-AGB phase. The mass-loss rates in the first and second types of wind are two or three orders of magnitude lower than the values evaluated by an empirical formula. The presence of a massive and slow wind of the fourth type suggests the possibility that the massive outflow observed in TP-AGB stars could be attributed to Alfvén wave-driven wind.
Many perspectives on Deborah Mayo's "Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars"

The new book by philosopher Deborah Mayo is relevant to data science for topical reasons, as she takes various controversial positions regarding hypothesis testing and statistical practice, and also as an entry point to thinking about the philosophy of statistics. The present article is a slightly expanded version of a series of informal reviews and comments on Mayo's book. We hope this discussion will introduce people to Mayo's ideas along with other perspectives on the topics she addresses.
Variational Analysis of Composite Models with Applications to Continuous Optimization

The paper is devoted to a comprehensive study of composite models in variational analysis and optimization the importance of which for numerous theoretical, algorithmic, and applied issues of operations research is difficult to overstate. The underlying theme of our study is a systematical replacement of conventional metric regularity and related requirements by much weaker metric subregulatity ones that lead us to significantly stronger and completely new results of first-order and second-order variational analysis and optimization. In this way, we develop extended calculus rules for first-order and second-order generalized differential constructions while paying the main attention in second-order variational theory to the new and rather large class of fully subamenable compositions. Applications to optimization include deriving enhanced no-gap second-order optimality conditions in constrained composite models, complete characterizations of the uniqueness of Lagrange multipliers, strong metric subregularity of Karush-Kuhn-Tucker systems in parametric optimization, and so on.
The Computational Complexity of Understanding Network Decisions

For a Boolean function $\Phi\colon\{0,1\}^d\to\{0,1\}$ and an assignment to its variables $\mathbf{x}=(x_1, x_2, \dots, x_d)$ we consider the problem of finding the subsets of the variables that are sufficient to determine the function value with a given probability $\delta$. This is motivated by the task of interpreting predictions of binary classifiers described as Boolean circuits (which can be seen as special cases of neural networks). We show that the problem of deciding whether such subsets of relevant variables of limited size $k\leq d$ exist is complete for the complexity class $\mathsf{NP}^{\mathsf{PP}}$ and thus generally unfeasible to solve. We introduce a variant where it suffices to check whether a subset determines the function value with probability at least $\delta$ or at most $\delta-\gamma$ for $0 0$, by a polynomial time algorithm unless $\mathsf{P} = \mathsf{NP}$ (this holds even with the probability gap).
H0LiCOW – X. Spectroscopic/imaging survey and galaxy-group identification around the strong gravitational lens system WFI 2033−4723

Galaxies and galaxy groups located along the line of sight towards gravitationally lensed quasars produce high-order perturbations of the gravitational potential at the lens position. When these perturbation are too large, they can induce a systematic error on H0 of a few per cent if the lens system is used for cosmological inference and the perturbers are not explicitly accounted for in the lens model. In this work, we present a detailed characterization of the environment of the lens system WFI 2033−4723 ($z_{\rm src} =\,$1.662, $z_{\rm lens}=\,$0.6575), one of the core targets of the H0LiCOW project for which we present cosmological inferences in a companion paper. We use the Gemini and ESO-Very Large telescopes to measure the spectroscopic redshifts of the brightest galaxies towards the lens, and use the ESO-MUSE integral field spectrograph to measure the velocity-dispersion of the lens ($\sigma _{\rm {los}}= 250^{+15}_{-21}$ km s−1) and of several nearby galaxies. In addition, we measure photometric redshifts and stellar masses of all galaxies down to i < 23 mag, mainly based on Dark Energy Survey imaging (DR1). Our new catalogue, complemented with literature data, more than doubles the number of known galaxy spectroscopic redshifts in the direct vicinity of the lens, expanding to 116 (64) the number of spectroscopic redshifts for galaxies separated by less than 3 arcmin (2 arcmin ) from the lens. Using the flexion-shift as a measure of the amplitude of the gravitational perturbation, we identify two galaxy groups and three galaxies that require specific attention in the lens models. The ESO MUSE data enable us to measure the velocity-dispersions of three of these galaxies. These results are essential for the cosmological inference analysis presented in Rusu et al.
Improvement of CMOS-MEMS accelerometer using the symmetric layers stacking design

This study presents a novel CMOS-MEMS capacitive type accelerometer design which consists of symmetric layers (4 metal and 3 dielectric layers) stacking to reduce the bending of suspended structures due to thin film residual stresses. Thus, the capacitance loss caused by the mismatch of sensing electrodes is reduced. Moreover, structures with symmetric layers stacking have less thermal deformation by temperature variation. A simple post-CMOS process including oxide wet-etching and dry XeF2 etching is established to fabricate the device. Measurement shows maximum bending deformation of a suspended 390µm×430µm structure is only 1µm, and mismatch of fixed and movable sensing electrodes is reduced to 1µm. The bending curvature has only ∼2% change as temperature increased 80°C. The sensitivity of this accelerometer is 1.46mV/G (in comparison, the accelerometer with asymmetric layers stacking structure has sensitivity of 0.07mV/G), and the noise level is 0.35mG/√Hz.
Malware and market share

This paper presents a game theoretic analysis of the relationship between an information technology platform’s market share, its level of security, and the extent to which malware creators (hackers) target a platform in order to proliferate via the platform’s network externalities. In equilibrium, a platform’s market share is shown to be the square root of the ratio of its competitor’s vulnerability to its own vulnerability. This implies that in order to maintain market share, platform leaders must make increasing investments in cybersecurity, thereby decreasing the platform’s vulnerability. Introduction Malware, a term that combines malicious with software, refers to a computer infection program designed to compromise, damage, or infiltrate a computer, server or network without the user’s knowledge or consent, often for profitable gain. Examples of self-replicating malware include viruses and worms. The potential for malware has been recognized since the dawn of personal computing itself. Hiltzik (1999) recounts a 1978 episode at Xerox’s revolutionary PARC research facility where an employee created a worm whose code became corrupted and caused scores of desktop computers connected to PARC’s Ethernet to repeatedly crash. According to a joint study by International Data Corporation and National Singapore University, for 2014 the annual cost of malware was expected to be over $491 billion a year (Robinson 2014). In addition, consumers would spend 1.2 billion hours dealing with the aftereffects of malware. The direct cost alone would rank malware as the 26 largest country in the world in terms of GDP. In such a high stakes environment it is necessary to understand the strategic incentives facing those who provide security for information technology platforms (e.g., PCs, tablets, smartphones), users who select this technology, and malware that targets users through platforms. As the famous bank robber Willie Sutton reportedly explained, he robbed banks, “because that’s where the money is. ” Similarly, hackers write malware to target where users are, and this is determined by the market share of a platform. Everything else held equal, hackers prefer a platform with a larger installed base (Honeynet Project 2004). This paper provides a game theoretic characterization of the relationship between market share, quality (security) of a platform, and the relative degree to which malware creators (hackers) target a platform. Two of the three of these variables: market share and the distribution of malware across platforms, are 11 The use of the term “platform” is widespread in the literature on the economics of two-sided markets, where a platform allows distinct user groups to interface, thereby providing each other with network benefits, often based on economies of scale. Technological devices or systems are quintessential examples of economic platforms because they create network externalities by matching users and application providers.