dataesr/scientific-paragraphs-categorization huggingface dataset:
242 For each protein the full MSA, high-throughput experiments, annotation and predicted sequen-243 tial features can be downloaded from the bottom of the page. Batch download is also available to 244 download the full database or different protein sets. aimed to collect high-throughput experimental data, PPI data on individual 248 proteins, predictions and localization information based on distant homologs. We noticed that the 249 amount of data from MS experiments differs highly, and therefore likely the quality also varies. 250 Not unexpected from high-throughput techniques applied to less-studied organisms, this can be 251 attributed to the quality of sample preparation, MS experimental techniques and most likely to 252 the sequences in the background databases. One striking finding was that the secretory datasets 253 contain a large number of proteins that are likely to take part in the housekeeping processes of 254.
fortunately often offline and no longer updated by now. LeishCyc Saunders et al. (2012) focused on biochemical pathways. LeishDB Torres et al. (2017) included coding genes and non-RNAs and provided new annotation to them. The cysteine protease database in Leishmania species Rana et al. (2012) was designed to find data related to cysteine protease and LeishBase was a structural database. There are a few active databases: Leish-ExP (http://www. hpppi. iicb. res. in/Leishex) (which has not so far been published in a peer-reviewed journal) contains proteins exclusively present in Leishmania. Leish-ExP incorporates localization tools, includes GO annotations and calculates physico-chemical properties of proteins. LmSmdB Patel et al. (2016) focuses on metabolic and biosynthetic pathways. TriTrypDB Shanmugasundram et al.
(2023) is a kinetoplastid database that is part of the VEuPathDB resource Amos et al. (2022). These databases contain a lot of experimental data and various tools to analyze eukaryotic pathogens, but they are mostly focused on genomic data -although proteomic datasets, and some protein prediction algorithms are also incorporated. There are also a handful of databases that include information on host-pathogen interactions: HPIDB Ammari et al. (2016), PHIDIAS Xiang et al. (2007) and PHI-base Urban et al. (2022) contain information about PPIs between the host and pathogen, while ImitateDB Tayal et al. (2022) specifically focuses on motif mimicry. These resources contain no or very little data about Leishmania.
Conversely, powerdomains and game semantics are more denotational approaches, aiming to ensure compositionality by construction; however, the mathematical structures involved are themselves very complex, typically involving many relations and constraints  [Abramsky and Melliès 1999; Melliès and Mimram 2007; Rideau and Winskel 2011]  that are not easy to implement in constructive logic (though there are some notable exceptions  [Koenig and Shao 2020; Oliveira Vale et al. 2022] ). Moreover, in all of the above-mentioned approaches, there are other tensions at play. For instance, how "deep" the embedding is affects the amount of effort needed to implement a formal semantics-"shallower" embeddings typically allow more re-use of metalanguage features, e. g. , meta-level function application can obviate the need to define and prove properties about a substitution operation; "deeper" embeddings can side-step meta-level limitations (such as Coq's insistence on pure, total functions) at the cost of additional work to define the semantics.
• We demonstrate how to use CTrees in two case studies: (1) to define a semantics for Milner's classic ccs and prove that the resulting derived equational theory coincides with the one given by the standard operational semantics, and (2) to model cooperative multithreading with support for fork and yield operations and prove nontrivial program equivalences. All of our results have been implemented in Coq, and all claims in this paper are fully mechanically verified. For expository purposes, we stray away from Coq's syntax in the body of this paper, but systematically link our claims to their formal counterpart via hyperlinks represented as ( ). The remainder of the paper is organized as follows. The next section gives some background about interaction trees and monadic interpreters, along with a discussion of the challenges of modeling nondeterminism, laying the foundation for our results.
We introduce the CTrees data structure and its main combinators in Section 3. Section 4 introduces several notions of equivalences over CTrees-(coinductive) equality, strong bisimilarity, weak bisimilarity, and trace equivalenceand describes its core equational theory. Section 5 describes how to interpret uninterpreted events in an ITree into "choice" branches in a CTree, as well as how to define the monadic interpretation of events from CTrees. Section 6 describes our first case study, a model for ccs. Section 7 describes our second case study, a model for the imp language extended with cooperative multithreading. Finally, Section 8 discusses related work and concludes.
Interaction trees and monadic interpreters Monadic interpreters have grown to be an attractive way to mechanize the semantics of a wide class of computational systems in dependent typed theory, such as the one found in many proof assistants, for which the host language is purely functional and total. In the Coq ecosystem, interaction trees  [Xia et al. 2020 ] provide a rich library for building and reasoning about such monadic interpreters. By building upon the free(r) monad  [Kiselyov and Ishii 2015; Letan et al. 2018] , one can both design highly reusable components, as well as define modular models of programming languages more amenable to evolution. By modeling recursion coinductively, in the style of Capretta's delay monad  [Altenkirch et al. 2017; Capretta 2005] , such interpreters can model non-total object languages while retaining the ability to extract correct-by-construction, executable, reference interpreters.
Consider the following imp programs:  1 ≜ while true do skip  2 ≜ ::= 0; ::=   3 ≜ ::=  Following a semantic model for imp built on ITrees in the style of  Xia et al. [Xia et al. 2020] , one builds a semantics in two stages. First, commands are represented as monadic computations of type itree MemE unit: commands do not return values, so the return type of the computation is the trivial unit type; interactions with the memory are (at first) left uninterpreted, as indicated by the event signature MemE. This signature encodes two operations: rd yields a value, while wr yields only the acknowledgment that the operation took place, which we encode again using unit. Indexing by the value type in the continuation of rd events gives rise to non-unary branches in the tree representing these programs.
[Yoon et al. 2022a] ). As hinted at by the combinator's name, the source program should be able to simulate the refined program. Fixing M to ctree F, this is expressed as ∀, refine h  ≲. However, one cannot hope to obtain such a result for an arbitrary h, because it could implement internal branches with an observable computation that can't be simulated by. We hence provide two illustrative families of well-behaved refinements, depicted on Figure  15. Constant refinements, implemented into ctree E, lift a function systematically, returning the same branch given the nature and arity of the branching node. Stateful refinements carry a memory into a piece of state St and implement the refinement into stateT St (ctree E) by using the current state to pick a branch, and update the state. A particular example of such a stateful refinement is a round-robin scheduler.
CASE STUDY: A MODEL FOR CCS We claim that CTrees form a versatile tool for building semantic models of nondeterministic systems, concurrent ones in particular. In this section, we illustrate the use of CTrees as a model of concurrent communicating processes by providing a semantics for Milner's Calculus of Communicating Systems (ccs)  [Milner 1989 ]. The results we obtain-the usual algebra, up-to principles, and precisely the same equivalence relation as the usual operational-based strong bisimulation-are standard, per se, but they are all established by exploiting the generic notion of bisimilarity of CTrees. The result is a shallowly embedded model for ccs in Coq that could be easily, and modularly, combined with other language features.
Model We define a denotational model for ccs using ctree actE void as domain, written ccs # in the following. As witnessed by this type, processes do not return any value, but may emit actions modeled as external events expecting unit for answer: Inductive actE ::= | act a : actE unit. Figure  18  defines the semantic operators associated with each construct of the language. They are written as over-lined versions of their syntactic counterparts, and defined over ccs #. The empty process is modeled as a stuck tree-we cannot observe it. Actions are directly defined as visible events, and thus the prefix triggers the action, and continues with the remaining of the process. As discussed in Section 2. 2, the delayed branching node fits exactly with the semantics of the choice operator in ccs, only progressing if one of the composed terms progresses.
Model As with the approach described in Section 2, the semantics of the language is defined in two stages. First, we represent statements as computations of type ctree (YieldE + SpawnE + MemE) unit, where YieldE and SpawnE are used to represent yield and fork respectively. The remaining statements of imp are defined in a standard way-we omit them. The new event families are defined as follows: Variant YieldE : Type → Type := | Yield : YieldE unit. Variant SpawnE : Type → Type := | Spawn : SpawnE bool. Yield carries no additional information and acts purely as a signal to yield control, and Spawn introduces a binary branch in the CTree, allowing us to store the asynchronous thread in one branch and the main thread that continues running in the other branch. Formally, these events are used to denote the corresponding statements: yield ≜trigger (Yield) fork 1 2 ≜  ← trigger (Spawn) ;; if  then 1 else 2 The second stage is more complex than the simple stateful interpretation of standard imp: we need to interpret the concurrency-related events.
Recent developments in machine learning and deep learning have become handy tools for urban planners and geoscience practitioners. Deep learning (DL) has gained significant attention in recent years for its ability to model complex patterns and make accurate predictions and it has the potential to address several urban planning challenges as well. Deep learning models such as generative adversarial networks (GAN) can approximate complex, high-dimensional probability distributions  18. GANs have achieved numerous state-of-the-art breakthroughs in the fields of computer vision  19  , natural language processing  20  , and more recently in urban science and geospatial domain  21, 22. Several GAN-based models have been proposed to simulate hyper-realistic urban land use maps and generate synthetic urban universe without considering the driving factors, for example, CityGAN  23  and MetroGAN  24.
CityGAN is the first step towards a flexible urban pattern simulator for more accurate projections on urbanization in regions where local data such as the location of services, population density, and other socio-economic factors are unavailable and difficult to obtain. In the case of a developing country like India, data on these spatial co-variates are difficult and expensive to compile and are often not available, where urban growth is more likely to occur. Hence the CityGAN was selected in our research based on the limited number of available urban maps. It is computationally efficient for generating Indian urban maps without considering feature datasets compared to other existing GAN models e. g. , MetroGAN, and UNet. To generate small and medium-sized Indian cities' urban maps with CityGAN, we collected the World Settlement Footprints (WSF 2019) maps which are publicly available and the best representations of urban patterns as input features  38, 39.
Then, we build a city image database of 503 small and medium Indian cities whose populations range between 20 and 500 K. Each city image represents 10. 5 × 10. 5 km covering the urban center and surrounding regions. We also explored existing spatial and statistical measures to evaluate the performance of CityGAN in Indian cities due to the complex nature of individual cities with varying structural and hierarchical properties  23. Furthermore, we used the Chatterjee Correlation Coefficient and Kernel Ridge Regression to establish the relationship between HSIs and network density and to build a prediction model for TI (particularly, road network density) using HSI based on real Indian cities. Then, we propose a hybrid model (namely, RidgeGAN) to predict the transportation index for simulated urban patterns.
Study area Our study focuses on small and medium-sized Indian cities (South Asia), one of the world's fastest-urbanizing regions. The selection of the study area involved the identification of the geographical location and corresponding demographic data for which the population data from the World Cities database (https:// simpl emaps. com/ data/ world-cities) were used. Approximately, 503 cities out of 1600 were selected for our study where the population size ranges between 20 and 500 k. The geographical location of study areas marked in the map of India is depicted in Fig. 2b  showing visualization of human settlement patterns and corresponding transportation networks (also see Fig. 2a, c ).
Data collection and preprocessing Settlement footprints and transportation network datasets of all the selected cities were collated from various sources over 2019. Settlement maps were procured from the global inventory built-up database called WSF, published by the German Aerospace Center (DLR). These are binary maps (urban area pixels have a value of 1 and non-urban area pixels have a value of 0) derived from multi-temporal space-borne satellites namely Sentinel-1 and Sentinel-2 data that aided in estimating the human settlement pattern of an urban area ( 10. 5 × 10. 5 km) with a resolution of around 10m/px. Open source GIS software (QGIS)  50  was used to pre-process (rectify, project, and crop) the images and build a city database. To measure Human Settlement Indices (HSI), existing landscape metrics were selected and computed using Fragstat software  57.
e. , almost square) patch. NLSI increases as the patch type becomes increasingly disaggregated and reaches 1 when the patch type is maximally disaggregated  33. We have used OpenStreetMap (OSM) data to calculate the road network density for real small and medium-sized 503 Indian cities. While OSM data is a valuable and freely available resource for road network information, our current study focused on utilizing OSM road transportation datasets that align closely with the scope of our research objectives. To compute the Transportation Index (TI), a layer of the road network that has been topologically cleaned and converted into polylines is a prerequisite. The application software used here is integrated with QGIS 3. 30  50  for this purpose. Individual cities and their corresponding road networks were extracted to assess the spatial patterns of the road systems corresponding to the respective cities.
As transportation measures need to be calculated in a metric system, a projected coordinate system was used. Instead of common WGS84-EPSG:4326, which uses degrees as a unit for distance, the Coordinate Reference System WGS84/UTM-EPSG was used here, to measure road length in meters. All categories of roads such as National highways, State highways, major roads, street roads, residential paths, footways, and service roads were included in this study. To measure the development of the urban road network, the network density of the respective cities was computed as follows: where L is determined from road maps and it has been calculated using an open field calculator in QGIS software  50. Network length specifies the total span of the road network and network density is measured according to the area occupied by road networks (city area), denoted as A, which refers to a geographical region characterized by a small and medium population.
City centers were obtained from the World City database, which provides the geographical location of each city. Squared shape settlement maps of cities from WSF images were obtained by considering this city center and buffer operation in QGIS software  50. ARP is denoted as h(d) or x(d) representing how much the settlement area changes as we go out from the city center. As indicated in Fig. 8b , we draw rings at a distance of d from the center and a width of d. ARP can be computed by averaging the total amount of settlement area within the rings of width d at a distance d from the city center. The region inside the ring is denoted as R(d) and each pixel inside the ring has some build-up area, that is the amount of urbanized area denoted as h(u, v), where (u, v) is a point inside the ring h(u, v) ∈ R(d) , with where the city center is denoted as (u 0 , v 0 ) and R(d) can be defined as the collection of all the two-dimensional points included within the ring, |R(d)| indicates the size of set R(d), and h(d) is the average radial profile of a city.
City centers were obtained from the World City Database, which provides the geographical location of each city. Squared shape settlement maps of cities from WSF images were obtained by considering this city center and buffer operation in QGIS software  50. There are several statistical measures that are used to assess the supervised regression models, for e. g. , mean squared error (MSE), mean absolute error (MAE), R-squared ( R 2 ), and adjusted R-squared (Adj R 2 ). MSE is the average squared difference between the predicted and actual values. It is a widely used metric that measures the quality of a regression model whereas MAE is the average absolute difference between the predicted and actual values. It is a robust metric that is less sensitive to outliers than MSE. R-squared measures the proportion of variance in the target variable that is explained by the model.
Generating human settlement area from WSF 2019 A dataset of real-time human settlement images was collected and pre-processed before training the GAN. We utilized squared-shaped settlement maps of cities from the WSF 2019 database. We clipped them representing 10. 5 km × 10. 5 km spatial extent and resized each image to 256 × 256 (43 m/px) for optimization purposes and to avoid overfitting. The final input dataset consists of 503 real images as binary maps and can be formulated as H i = h 1 ,. , h n , with H i ∈ R WXW and W = 256 and n = 503. The element of H i is an urban binary map (1 and 0 represent urban and non-urban areas respectively). We chose 503 binary maps of Indian cities to align with the objectives of the Integrated Development of Small and Medium Towns (IDSMT) National Scheme.
This selection ensures that the model is trained on a dataset that is both representative and manageable for effective training. The generator network is trained to generate synthetic urban settlement images by generating random noise and transforming it into an urban image whose distribution matches the real urban images. The discriminator network is trained to distinguish between real and synthetic binary images. CityGAN architecture contains a Generator and Discriminator, both are deepCNN with different weight vectors. In Generator, we first append Inverse CNN layers along with batch normalization, and Relu activation function, and add tan hyperbolic activation function. For Discriminator, we used a very similar architecture, with LeakyRelu, a nonlinear activation function instead of Relu, and simple CNN instead of inverse CNN.
During parameter optimization, we use the Adam optimizer with a learning rate of 0. 0002 and weight (decay rate) parameter Beta ranges from 0. 5 to 0. 999. We initialize the train loop with 25 sets of epochs. Both the G and D are trained with a loss function as discussed in the main manuscript. Generator (G) takes a random noise vector Z noise as input, which deterministically changes (e. g. , by passing it through successive deconvolutional layers if G is a deep CNN) to generate a sample fake human settlement image H fake = G(z). Then the Discriminator (D) accepts an input image H (which can be an actual human settlement image ( H real ) from an empirical dataset or generated image ( H fake ) synthetically by a generator and outputs the source probability that H is either sampled from the real distribution ( H real ) or produced by generator ( H fake ).
Having trained a generator (G) (refer Fig. 3a ), we generated synthetic Indian urban settlement patterns of 500 binary images using the CityGAN model. Despite the capability to generate thousands of synthetic images using the CityGAN model, we deliberately limited the output to only 500 images to balance the computational resources and the need for a representative dataset of small and medium-sized Indian settlement patterns. We found that the generated 500 binary images provide an effective representation of the diversity and characteristics of the Indian cities. Figure  4  illustrates randomly selected real cities (Fig. 4a ) and simulated urban patterns (in Fig. 4b ). On a visual inspection, simulations are practically indistinguishable from the actual urban patterns, with realistic (1) R(d) ≡ {(u, v) | (u -u 0 ) 2 + (v -v 0 ) 2 > d 2 and (u -u 0 ) 2 + (v -v 0 ) 2 ≤ (d + �d) 2 }; (2) h(d) = 1 |R(d)| (u,v)∈R(d) h(u, v); densities and complexity of settlement patterns.
Prediction of transportation index A supervised machine learning approach (Kernel Ridge Regression) was implemented to predict road network density for given settlement patterns (refer to Fig. 3d ). We compare the performance of various other supervised regression models such as support vector regression (SVR), decision tree (DT), gradient boosting (GB), multilayer perceptron regression (MLP), XGboost (XGB), linear regression (LR), random forest (RF) and simple ridge regression (RR) to select the best-fit model. dataset. The results of our model and comparison with other models are summarized in Table  4. Experimental result shows that KRR outperforms all other eight state-of-the-art regression models, in terms of the highest R 2 and adjusted R 2 and lowest error metrics (MSE and MAE values).
This study proposes a hybrid model to predict road network density for small and medium-sized settlement patterns in India. We used the publicly available WSF datasets and CityGAN model to build an unsupervised model to simulate realistic Indian urban patterns. The average radial profile was used to compare real against simulated cities to validate the performance of CityGAN. Also, we used the K-means technique to cluster the radial profiles with the optimal number of clusters equal to 10 for both actual and synthetic scenes using a straightforward fraction of sum-of-squares reasoning. Landscape structures of these generated cities were measured in terms of human settlement indices using spatial landscape metrics. Then, various supervised machine learning models were implemented for predicting transportation indices from human settlement indices based on real city datasets.