dataesr/scientific-paragraphs-categorization huggingface dataset:
A typical principled approach to reconstructing more E-mail: niall. jeffrey. 15@ucl. ac. uk † github. com/NiallJeffrey/DeepMass accurate mass maps in the presence of noisy, masked shear data is to use physically motivated priors. In  Jeffrey et al. (2018b)  it was shown that using either Gaussian priors or "halo model" sparsity priors for κ improved the accuracy of the reconstructions with Dark Energy Survey Science Verification (DES SV) data. Implemented methods include using log-normal  (Böhm et al. 2017)  priors or E-mode priors  (Mawdsley et al. 2019). However, all of these priors take functional forms that only approximate the true object of interest, the prior on the convergence field P(κ|M) (with model assumptions M). These approximations are necessary because we cannot represent the probability distribution of the non-linear density field in closed form.
The full architecture and code can be seen online: DeepMass †. We have added Batch Normalization layers  (Ioffe & Szegedy 2015)  after each convolutional layer; without this, training often became stuck in local minima of the cost function with respect to the parameters Θ. For all layers, except for the final, we use the rectified linear unit (ReLU) activation. In the final layer we use a sigmoid function, which forces the output to be between 0 and 1 (inputs and outputs are correspondingly rescaled). For simplicity and memory efficiency, we aimed to work with real (32-bit) numbers, thus necessitating an initial operation acting on the complex shear γ. The best results came from using a fixed Wiener filter operation before the first convolution (rather than KS, as might be expected). This is equivalent to the first layer having M j=0 = W and ρ = 1, with no free parameters.
l-picola simulations The training data is derived from 74 independent dark matter simulations, with each simulation covering an octant of the sky. The simulations used a standard flat ΛCDM cosmological model with H 0 = 70 km Mpc -1 s -1. The scalar spectral index and baryon density were fixed at n s = 0. 95 and Ω b = 0. 044 respectively. The values of Ω m and the amplitude parameter σ 8 are distributed on a non-Euclidean grid with distances between points giving a density according to our prior P(σ 8 , Ω m ) as shown in figure  1. Weak lensing constraints are most sensitive to combinations of this pair of parameters, so we avoid overfitting to a single cosmology by varying them in the training data. To generate a convergence map from a simulation, the matter particles were binned using the healpix (Górski et al.
We report the first statistical detection of X-ray emission from cosmic web filaments in ROSAT data. We selected 15,165 filaments at 0. 2<z<0. 6 ranging from 30 Mpc to 100 Mpc in length, identified in the Sloan Digital Sky Survey (SDSS) survey. We stacked the X-ray count-rate maps from ROSAT around the filaments, excluding resolved galaxy groups and clusters above the mass of ∼ 3 × 10 13 M as well as the detected X-ray point sources from the ROSAT, Chandra, and XMM-Newton observations. The stacked signal results in the detection of the X-ray emission from the cosmic filaments at a significance of 4. 2σ in the energy band of 0. 56-1. 21 keV. The signal is interpreted, assuming the Astrophysical Plasma Emission Code (APEC) model, as an emission from the hot gas in the filament-core regions with an average gas temperature of 0.
Gas density and temperature Using the stacked X-ray count-rate measured at the six energy bands, we performed an X-ray spectral analysis using the Python X-ray spectral analysis package (PyXspec) interface with the Xray spectral analysis package (XSPEC)  (Arnaud 1996)  to estimate the average gas density and temperature at the cores of the 15,165 filaments. The response file, 'pspcc_gain1_256. rsp', is provided 5. For the model of the X-ray emission, we used the Astrophysical Plasma Emission Code (APEC) model  (Smith et al. 2001)  with two free parameters: the normalization of surface brightness and temperature. The metallicity of gas was assumed to be 0. 2 of the solar value  (Anders & Grevesse 1989) , based on the measurements in peripheries of intracluster medium (ICM)  (Mernier et al. 2017 (Mernier et al.
Simulating X-ray spectrum of SRG/eROSITA We simulated X-ray energy spectra from gas in all the 15,165 filaments as they would be observed by SRG/eROSITA, including their lengths, angles, and redshifts (that determine their surface brightness on the sky and spectral shifts along the energy) via the PyXspec interface with XSPEC. We evaluated the X-ray emission at the cores of the filaments using the same model as in Sect. 3. 2, with the normalization and temperature from our measured values. We also included masks of galaxy clusters, critical points  The 1σ uncertainties are estimated by a bootstrap resampling. Cyan: Xray emission at the cores from 1,000 random sampling. The 1σ uncertainties are estimated by computing a standard deviation of 1,000 random sample. The data points from the random sampling shift slightly to the right on the X-axis for a visualization.
Systematics We investigated possible biases on the measurement of the X-ray signals from the filaments in the ROSAT data due to resolved and unresolved galaxy groups and clusters in the foreground and background as in T20. The contribution from resolved galaxy clusters was investigated by varying the radius of the mask. We find that the resolved clusters are well covered by our mask and the contamination to our measurement is minor. In addition, the contamination from unresolved groups and clusters, which is mostly handled by the subtraction of the background signal, was investigated. The potential residual contamination is tested through the random sampling. The result of the random sampling is consistent with zero as shown in Fig. 1 , suggesting that our results are not biased by residual contaminations from unresolved groups and clusters.
In the test, the spectral index is varied between 1. 4 and 1. 7. The result of the fitting shows that the contribution of the power-law component is only ∼ 3 × 10 -17 erg cm -2 s -1 deg -2 (0. 5-2. 0 keV) compared to ∼ 6 × 10 -14 erg cm -2 s -1 deg -2 (0. 5-2. 0 keV) of the APEC component, and we found that the contribution of point sources inside filaments was minor. To complete our analysis of the systematics related to point sources, we tested, using the Akaike's Information Criterion (AIC) method  (Akaike 1973) , whether one out of the two models (APEC and power law) can be ruled out statistically. We find that the probability that the APEC model is preferred is 64%. This means that despite a slight preference for the APEC model, both models fit the data almost equally well. In this test, the selection of the model is so far limited by the quality of the data used; more sensitive measurements such as those of SRG/eROSITAwould be needed for further assessment.
Discussion We measure for the first time, with ROSAT maps, the X-ray emission from the cores of large (> 30 Mpc) cosmic filaments. Using ROSAT data,  Briel & Henry (1995)  searched for a X-ray signal from shorter filaments between pairs of galaxy clusters with a tangential distance of ∼20 Mpc/h on the sky. These authors accumulated the X-ray photons between the cluster pairs assuming that the filaments between the cluster pairs are almost straight, and they ended up with an upper limit that is two orders of magnitude larger than ours. One reason for the difference between our findings and those of  Briel & Henry (1995)  is due to the small number of filaments stacked in that study (∼40), as compared to the stack of ∼15,000 filaments in the present study. As shown in Fig. 4 , even in the case of SRG/eROSITA data (30 times more sensitive than ROSAT), there should be statistically enough filaments for a significant detection.
1 keV. Compared to these statistical studies, our measurement of gas temperature shows a relatively higher value. Considering that our gas temperature is estimated at the cores (< 2 Mpc) of the filaments, our result may imply that the gas temperature is higher at the cores, compared to the outskirts. On the other hand, the value obtained from the tSZ measurements in the Planck data is averaged over ∼5 Mpc, owing to the limited angular resolution. For a definitive statement on the gas temperature in filaments, more sensitive measurements are needed.
We find that the average central overdensity is δ = 30 ± 15, assuming a cylindrical filament with a density distribution following a β-model with β=2/3. We compare our measurement of gas density with other statistical measurements with the weak-lensing and the tSZ, and find that they are all consistent. We also estimate the average gas temperature in the filament core region to be 0. 9 +1. 0 -0. 6 keV and find that it is a bit larger than the study of gas in large cosmic filaments with the tSZ by T20. This may imply that the gas temperature is higher at the cores compared to the outskirts, considering that the gas temperature from X-rays is estimated in a region < 2 Mpc from the filament spines whereas the values in the tSZ measurements are the average beyond the distance (∼5 Mpc). A definitive statement on the gas temperature in filaments from the X-rays necessitates higher quality data such as those from the SRG/eROSITA all-sky survey.
As a matter of fact, when we model the emission from cosmic filaments using the gas density and temperature estimated with the ROSAT data, stacked filaments can be detected with very high significance A&A proofs: manuscript no. main (i. e. , ∼ 4σ for only 100 filaments and up to ∼ 46σ for a sample of 15,165 filaments as used here). With such high quality data as SRG/eROSITA, we show that we can detect lower-temperature gas of ∼0. 3 keV by stacking ∼2,000 filaments, which would lead to a 5σ detection Simulated energy spectra for SRG/eROSITA from the gas at the cores of filaments. In the top panel, the spectra are calculated with an overdensity of 30, a temperature of 0. 9 keV, and a metallicity of 0. 2Z. The stacked spectra (red curves) display the expected signal from the brightest N filaments (with N = 10 to 15,165 ).
Acknowledgements. The authors thank an anonymous referee for the useful comments and suggestions. This research has been supported by the funding for the  Baryon Picture of the Cosmos (ByoPiC)  project from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  grant agreement  ERC-2015-AdG 695561. The authors acknowledge fruitful discussions with the members of the  ByoPiC  project (https://byopic. eu/team). This publication has made use of the SZ-Cluster Database (http://szcluster-db. ias. u-psud. fr/sitools/client-user/SZCLUSTER_DATABASE/project-index. html)operated by the  Integrated Data and Operation Centre (IDOC) at the Institut d'Astrophysique Spatiale (IAS)  under contract with  CNES  and  CNRS. Maps are provided in HEALpix (http://healpix.
Conclusion We have implemented a quick-and-dirty differential gene expression analysis pipeline for non-model organisms without a reference transcriptome. It uses quasi-mapping to rapidly map RNA-seq reads to a reference protein database, followed by a simple counting step the result of which can be fed to standard differential analysis tools like DESeq2. It is computationally super light-weight compared to the conventional approach of first building a transcriptome assembly. The quasi-mapping step itself is fast compared to other alignment/mapping techniques, but there is room for improvement in its sensitivity. Declarations Ethics approval and consent to participate Not applicable.
We thereby prove the linear convergence rate of CQ-GGADMM, and show its effectiveness by simulations, in terms of convergence speed, total communication cost, and transmission energy consumption. Contributions. The major contributions of this work are summarized as follows. • We propose CQ-GGADMM, a primal-dual decentralized learning framework utilizing censoring, quantization, and GADMM for any bipartite and connected network topology graph (Algorithm 1 in Sec. III). • We prove that CQ-GGADMM converges to the optimal solution for convex loss functions (Theorem 1 in Sec. IV). • We identify the network topology conditions under which CQ-GGADMM achieves a linear convergence rate (Theorem 2 in Sec. IV) when the loss functions are strongly convex. • Numerical simulations have corroborated that in linear and logistic regression tasks using synthetic and real datasets, CQ-GGADMM achieves the same convergence speed at significantly lower communication rounds and several orders of magnitude less transmission energy, compared to C-GGADMM and Censored ADMM (C-ADMM) in  [13].
The optimization problem is given by (P1) Θ * := arg min Θ N n=1 f n (Θ), (1) where Θ ∈ R d×1 is the global model parameter and f n : R d → R is a local function composed of data stored at worker n. Problem (P1) appears in many applications of machine learning, especially when the dataset is very large and the training is carried out using different workers. The connections among workers are represented as an undirected communication graph G having the set E ⊆ V × V of edges. The set of neighbors of worker n is defined as N n = {m|(n, m) ∈ E} whose cardinality is |N n | = d n. We start by making the following key assumption. Assumption 1. The communication graph G is bipartite and connected. Under Assumption 1, following the worker grouping of GADMM  [11] , workers are divided into two groups: a head group H, and a tail group T.
V. NUMERICAL RESULTS To validate our theoretical results, we numerically evaluate the performance of CQ-GGADMM compared with GGADMM, C-GGADMM, and C-ADMM  [13]. Note that C-ADMM performs censoring on top of the Jacobian and decentralized version of the standard ADMM. For the tuning parameters, we choose the values leading to the best performance of all algorithms. Model and Datasets. All simulations are conducted using synthetic and real datasets. For the synthetic data, we used the datasets that were generated in  [21]. We consider two decentralized consensus optimization problems: (i) linear regression, and (ii) logistic regression. The details about the datasets used in our experiments are summarized in Table  I. For each dataset, the number of samples are uniformly distributed across the N workers.
C. Impact of the Network Graph Density To study how the network graph density (the node degree) affects the performance of the proposed approach, we conduct an experiment using linear regression on real dataset under different graph topologies. In particular, we consider two graphs with different density as shown in Fig. 6. The first graph, denoted by Graph 1, is a sparse graph (generated with p = 0. 2), where each worker has a few links (communicating with low number of neighbouring workers). For example, worker 12 communicates only with one neighbour (worker 8). On the other hand, the dense graph (Graph 2) is generated with a connectivity ratio p = 0. 4 where each worker has at least three links (three neighbours). We can see from Fig. 6  that a denser graph leads to faster convergence for all algorithms since each worker uses more information per iteration.
D. Impact of the non-IIDness To study the impact of non-IIDness on the convergence of the different algorithms, we artificially create a non-IID 1536-1276 (c) 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www. ieee. org/publications_standards/publications/rights/index. html for more information. linear regression synthetic 50 1200 Body Fat  [34]  linear regression real 14 252 synth-logistic  [21]  logistic regression synthetic 50 1200 Derm  [34]  logistic regression real 34 358 TABLE I: List of datasets used in the numerical experiments. partitioned data from the Derm dataset by first sorting the data by label and then dividing them between the workers. In Fig. 7 , we study the effect of the non-IIDness on the performance of the different algorithms by plotting the loss as a function of the number of iterations, communications rounds, transmitted bits, and sum energy.
Although it is clear that the non-IIDness of the data hurts the performance of all algorithms, CQ-GGADMM still provides significant savings in terms of communication rounds, and especially in transmitted bits and sum energy compared to the other baselines. Since the distribution of each local dataset differs from the global distribution, each worker's local objective can be different. As a result, the local updates can drift from the global optima. Hence, the averaged model may also be far from the global optimal, especially when the difference between the local updates is significant, i. e. the degree of heterogeneity between the local datasets is high. Consequently, the global model has significantly lower performance in the non-IID setting than the IID one.
Spot is a C ++ 17 library for LTL and ω-automata manipulation, with command-line utilities, and Python bindings. This paper summarizes its evolution over the past six years, since the release of Spot 2. 0, which was the first version to support ω-automata with arbitrary acceptance conditions, and the last version presented at a conference. Since then, Spot has been extended with several features such as acceptance transformations, alternating automata, games, LTL synthesis, and more. We also shed some lights on the data-structure used to store automata. Artifact: https://zenodo. org/record/6521395.
Availability, Purpose, and Evolution Spot is a library for LTL and ω-automata manipulation, distributed under a GPLv3 license. Its source code is available from https://spot. lrde. epita. fr/. We provide packages for some Linux distributions like Debian and Fedora, but other packages can also be found for Conda-Forge [17] (for Linux & Darwin), Arch Linux, FreeBSD. Spot can be used via three interfaces: a C ++ 17 library, a set of commandline tools that give easy access to many features of the library, and Python bindings, that makes prototyping and interactive work very attractive. Our web site now contains many examples of how to perform some tasks using these three interfaces, and we have a public mailing list for questions. In our last tool paper [21], Spot 2. 0 had just converted from being a library for working on Transition-based Generalized Büchi Automata and had become a library supporting ω-automata with arbitrary  41]  acceptance conditions, as enabled by the development of the HOA format [5].
Use-cases of Spot, and Related Tools As it is a library, there are many ways to use Spot. We are mostly aware of such uses via citations 2. Historical and frequent uses-cases are to use Spot for translating LTL formulas to automata (Winners of the sequential LTL and parallel LTL tracks of RERS'19 challenge  [26]  both used Spot to translate the properties into automata, many competitors on the Model Checking Contest  [28]  also use   [20]  0. 9 Support for some PSL operators. 1. 0 Command-line tools, mostly focused on LTL/PSL input  [19]. Includes ltlcross, a clone of LBTT  [42]. Python bindings.
g. ,  18, 25, 32, 33] , but we also see some new applications built on top of ω-automata algorithms from Spot [e. g. ,  12, 13]. The projects that have the largest intersections of features with Spot seem to be GOAL  [43]  and Owl  [30]. These are two Java-based frameworks that deal with similar objects and provide a range of algorithms. Owl and Spot share a similar and traditional Unix view of the command-line experience, where multiple commands are expected to be chained with pipes, and they both communicate smoothly via the HOA format  [5]. GOAL is centered on a graphical interface in which the user can edit automata, and apply algorithms listed in menu entries. Using GOAL from the command-line is possible by writing short scripts in a custom language. As far as interfacing goes, the most important feature of Spot is probably that it exposes its algorithms and data structures in Python.
3 The class twa graph, introduced in Spot 2. 0, implements an explicit, graphbased, representation of an automaton, in which states and edges are designated by integers. This makes for a much simpler interface  4  and usually simplifies the data structures used in algorithms (since states and edges can be used as indices in arrays). The data structure is best illustrated by using the show storage() method of the Python bindings, as shown by Fig. 1. A twa graph is stored as two C ++ vectors: a vector of states, and a vector of edges. For each state, the first vector stores two edge numbers: succ is the first outgoing edge, and succ tail is the last one. These number are indices into the edge vector, which stores five pieces of information per edge. Four of them are related to the identity of the edge: src, dst, cond, acc are respectively the source, destination, guard, and color sets of the edge.
The bottom of the figure shows that the automaton has prop state acc set, which means that the automaton is meant to be interpreted as using state-based acceptance. Colors are still stored on edges internally, but all edges leaving a state have the same colors. Seeing that the condition is co-Büchi (Fin(0)), the display code automatically switched to the convention of using double-circles for rejecting states. Destinations with the sign bit set are called universal destination groups and appear as pink in the figure. There are two groups here: ~0 and ~3. The complement of these numbers can be used as indices in the dests vector, that actually store the destination groups. At the given index, one can read the size n of the destination group, followed by the state number of the n destinations.
Algorithms that work on alternating automata need to be able to iterate over all destinations of an edge. The process of checking the sign bit of the destination to decide if its a group, and to iterate on that group is hidden by the univ dests() method: Note that this code works on non-universal branches as well: if e. dst is unsigned, univ dests(e) will simply iterate on that unique value. Spot has two alternation removal procedures. One is an on-the-fly implementation of the Breakpoint construction  [37]  which transforms an n-state alternating Büchi automaton into a non-alternating Büchi automaton with at most 3 n states. For very weak alternating automata, it is known that a powerset-based procedure can produce a transition-based generalized Büchi automaton with 2 n states  [23] ; in fact that algorithm even works on ordered automata  [11] , i.
Extending Automata via Named Properties Spot's automata have a mechanism to attach arbitrary data to automata, called named properties. (This is similar to the notion of attributes in the R language. ) An object can be attached to the automaton with: aut->set_named_prop("property-name", new mytype(. )); and later retrieved with: mytype* data = aut->get_named_prop<mytype>("property-name"); Ensuring that mytype is the correct type for the retrieved property is the programmer's responsibility. Spot has grown a list of many such properties over time. 5  For instance automaton-name stores a string that would be displayed as the name of the automaton. The highlight-edges and highlight-states properties can be used to color edges and states. The state-names is a vector of strings that gives a name to each state, etc.
Online Application for LTL Formulas The Python ecosystem makes it easy to develop web interfaces for convenient access to a subset of features of Spot. For instance Fig. 4  shows screenshots of a web application built using a React frontend, and running Spot on the server. It can transform LTL formulas into automata, can display many properties of a formula (membership to the Manna & Pnueli hierarchy  [34] , Safety/Liveness classification  [2] , Rabin and Streett indices  [14] , stutter-invariance  [36] ), or simply compare two formulas using a Venn diagram. This application has been found to be useful for teaching about LTL and its relation with automata, but is also a helpful research tool.