dataesr/scientific-paragraphs-categorization huggingface dataset:
As to the enumerative lifting procedure described in Section 2. 2, instead of solving the recourse problem exactly (i. e. , solving the KP as an MIP) we only solve its LP relaxation, cf. Step 4 of Algorithm 3. In this way we obtain, with limited computational effort, a valid dual bound that can be used for lifting according to the observation in Remark 2. As described in Section 3. 3, a set P satisfying the conditions for lifting can be removed from consideration if it includes items that cannot be used together in some feasible recourse solution. To eliminate such P , we simply check the knapsack constraint for the recourse solution y having y i = 1, ∀i ∈ P and y i = 0, ∀i / ∈ P. If it is violated, we simply move to the next iteration. 5. 1. 2 TRS Knapsack Interdiction Dataset. In this section, we consider the 150 instances introduced by  Tang et al.
CCLW Knapsack Interdiction Dataset. Our second benchmark for the KFG is derived from the instances proposed by  Caprara et al. (2016)  for the Knapsack Interdiction Problem. In this case too, we introduce an additional fortification level, and impose a cardinality constraint for the leader. For interdiction costs and budgets we used the data available in the original instances. The number of items takes value in {35, 40, 45, 50, 55} and for each size there are ten instances in which the interdiction budget increases with the ID of the instance, 1 ≤ ID ≤ 10. We solved these 50 instances for B F ∈ {3, 5}, thus producing a set of 100 benchmark instances denoted as CCLW. Figure  2  shows the cummulative distribution of root gaps and solution times for the instances in this benchmark set. It can be seen that bound based lifting does not have an effect in both measures.
This is mostly due to the fact that we are able to produce only a few lifted cuts, as the value of Φ(x) -z is usually larger than d i in our experiments. Enumerative lifting does not contribute to decreasing the solution times because of its time complexity, which depends on the interdiction budget; however this strategy decreases the root gap on average. Note that the root gap reduction is larger for smaller budget levels. We also observe smaller number of B&C nodes when enumerative lifting is activated, although we do not report details about this kind of measure here. Similar to the results on the TRS data set, the most effective component is greedy integer separation (G). We observe in our experiments that Algorithm 2 is able to find good interdiction strategies quickly, which makes the separation process more efficient compared to the default setting where we stop solving (SEP) once a violated fortification is found.
Nevertheless, in this case, the separation problem can be solved extremely efficiently, which overall leads to improved performances. Tables  2  and 3  report the average and maximum solution times required by our algorithm with setting IBEG. These figures are compared with the results reported in  Lozano and Smith (2017) , denoted as LS, which were obtained using a machine with an Intel Core i7-3537U CPU (two cores) running at 2. 00 GHz and with a memory limit of 2 GB. The mixed-integer linear programming solver is Gurobi 5. 6, and the algorithm was coded in Java. In both tables, rows are associated to a network size and a configuration  for the fortification and interdiction budget levels, whereas the three blocks of columns refer to different cost-delay configurations. Table  2  contains the results for smallest three cost-delay configurations, and shows that IBEG yields smaller average and maximum solution times except for the smallest instances.
Acknowledgments This research was funded in whole, or in part, by the  Austrian Science Fund (FWF) [P 35160-N]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. It is also supported supported by the  Linz Institute of Technology  (Project  LIT-2019-7-YOU-211 ) and the  JKU Business School. The work of the third author was supported by the  Air Force Office of Scientific Research  under Grant no. FA8655-20-1-7012.
This paper proposes the method pELECTRE Tri, a novel probabilistic Multi-Criteria Decision Making (MCDM) method using the traditional ELECTRE Tri, probability distribution, and Monte Carlo simulation to support informed decision-making in complex and uncertain decision environments. The proposed method is illustrated through a case study involving the renovation of three social housing buildings. The paper provides new insights into the application of probabilistic sorting MCDM in the context of energy efficiency in buildings and highlights the benefits of using probabilities rather than crisp values to categorize alternatives enabling stakeholders to make better use of available resources, especially when dealing with a large dataset of energy measures with different features. The methodology implemented in Python (DOI: 10.
The ELECTRE Tri method was used to classify 16 distinct sustainable energy technologies for electricity generation based on their compliance with the Clean Development Mechanism (CDM) using six different economic, environmental, and social criteria to sort the sixteen alternatives into three priority categories  [76]. A comparative evaluation was conducted by using ELECTRE Tri and the Data Envelopment Analysis (DEA) to evaluate 41 biogas industries in Austria. The industries were classified into four categories based on economic, environmental, and social criteria  [77]. In Portugal, the ELECTRE Tri method was used to assess the energy efficiency of school buildings  [78]. The ELECTRE Tri-nC technique was used to aid decision-making by categorizing alternative energy retrofit measures into three distinct categories for public buildings in the Apulia region of Italy  [23].
A multi-criteria analysis is a suitable approach for comparing and defining a retrofit program in order to select alternatives for the renovation of social housing. In this context, the ELECTRE Tri method is regarded as the most appropriate approach due to the following reasons. It is well-suited for complex problems with multiple criteria and alternatives, and makes use of a variety of qualitative and quantitative evaluation scales. It categorizes alternatives based on performance, rather than ranking them from best to worst, which enables the decision-makers to rank them subjectivity. The use of boundary reference profiles and categorization provides information on overall performance, allowing for an absolute classification of each alternative. This characteristic is especially important when choosing an energy renovation alternative which should have a high overall performance rather than just relative performance.
Acknowledgments: The project was carried out in part within the company  3F-Immobilière Rhône Alpes  in collaboration with  Jean-Michel Paris , Director of  Patrimony and Urban Renewal , and  Gaël Wiederkehr   Project Manager Patrimony. This work was made possible by collaboration with the following structures: ALTEREA, a consulting company specializing in energy retrofitting and sustainable buildings; TIPEE, an engineering company specializing in energy and environmental performance; OGGA, a company specialized in the design, manufacturing, and marketing of energy measurement and management solutions for buildings; and GBS, a sociology firm specializing in the ecological and energy transition of buildings.
The aim of this work is to compare the performance and ease of programming of the various data transfer strategies provided by SYCL 2020: buffers/accessors on one hand and the different storage types exposed by Unified Shared Memory (USM) on the other hand. We measured the relative performance of USM exclusively located either on the host (USM host) or on the device (USM device), or automatically managed and moved (USM shared ). We also tried to quantify the impact of formatting data in a GPU-friendly manner as opposed to using a regular pointerbased structure in which the C++ allocator was replaced by the SYCL allocator sycl::malloc. We first made a PCIe-bound micro-benchmark to test the SYCL memory models with a simple access pattern. We then switched to a real use-case provided by Traccc, a research project associated to the ACTS particle tracking library.
Introduction ACTS is an experiment-independent C++ toolkit for charged particle track reconstruction in high energy physics experiments. Within this context, the Traccc project investigates how to take advantage of the increased variety of available hardware at CERN, such as GPUs. How to efficiently make existing code run on accelerators is an open-ended question, both performancewise and in terms of time spent writing software. SYCL is a royalty-free, cross-platform abstraction layer that enables code for heterogeneous processors to be written using standard ISO C++17 with the host and kernel code for an application contained in the same source file. SYCL also provides two memory models (USM and buffers/accessors) allowing for CPU data-structures to be used almost untouched on GPUs, yet also allowing data-structures to be formatted in a GPU-friendly way.
By using SYCL, we will try to evaluate the impact of using almost untouched CPU datastructures for GPUs, compared to data-structures commonly used for GPU programming, both with a simplified benchmark and with a real life algorithm from Traccc. We will also compare the different memory models offered by SYCL. Every benchmark was run on the same system: a Debian GNU/Linux 11. 0 based server with 512 GB DDR4-2400, an AMD EPYC 7502 CPU with 32 cores, 64 threads, a PCIe x16 bus with a theoretical bandwidth of 16 GB/s and an NVIDIA Quadro RTX 5000 having 16 GB GDDR6 and a theoretical memory bandwidth of 448 GB/s. The SYCL code was run by hipSYCL 0. 9. 1-20210702 amd64, using the CUDA 10. 1 back-end. We felt hipSYCL to be an interesting SYCL implementation to try out first, rather than the more mainstream Data Parallel C++ (DPC++) from Intel, as the SYCL standard was made to avoid monopoly from any single implementation.
Yet this comes with a price. USM host SYCL allocation is expensive, and the data must be transferred from host to device each time it is accessed, as demonstrated by the kernel run 2. Usage of USM host may therefore only be recommended in use cases where data is accessed only once and allocations can be reused. In contrast, USM shared exhibits negligible allocation overhead, but the automatic data migration process significantly impacts both the fill and kernel steps. By profiling the execution of this program using NVIDIA's Visual Profiler (nvvp), we found that the memory copy still goes on in an asynchronous way until the first kernel has ended. The SYCL implementation does not know we need to access memory in a respectively read-only and write-only mode. This results in a large number of page faults, that only finish once the whole dataset has been accessed by the first kernel run.
SparseCCL with flat arrays We started to structure the data in a relatively device-friendly fashion. It is flattened into 2 large contiguous arrays, easily copyable to and from the device: one for the input data and one for the output, accounting for respectively 1664 MB and 833 MB. Results are shown on Figure  2. The USM host kernel takes a very long time to execute because instead of accessing device local memory, or even caches, an I/O request on the PCIe bus is issued each time the kernel needs to access memory, leading to extremely poor performance. To avoid masking the difference between the other options, we therefore opted not to display in in the graph. With that outlier out of the way, the comparison of the three other options ends up qualitatively similar to the micro-benchmark case.
Related Work Bundle recommendation can be defined as the problem of selecting the best group of items from a potentially very large dataset according to some user preferences. This type of recommendation method can involve a series of tasks such as detecting, completing, and ranking bundles as well as generating bundle explanations and bundle auto-naming  [21]. Bundle recommendation typically aims to generate bundles of associated products that users tend to consume as a whole under certain circumstances. Association between products are discovered, for instance, by exploiting relations among bundle products as well as between products and users. A recommendation system based on bundles can provide several benefits such as it can help enhance user experience (e. g. , complementary items) as well as increase sales revenue for sellers (e.
This rationale led to the formulation of the third research question, and to tackle this problem, we conducted 1,000 simulations where we randomly removed a fraction of items considering them as unavailable. For each simulation and production area, we extracted the bundles having the highest score according to the Choquet integral and the weighted sum respectively. We then computed the regret of the weighted sum recommendation as the difference in environmental friendliness score (which, as shown in Section 5. 4, can be formalized as the Choquet integral), between the two bundles. Following the conclusion of the simulations, an analysis of the collected data was performed. The distribution of regret across the production areas is illustrated in Figure  6. It is evident that the limited availability of certain products has resulted in instances of regret in the recommendations, especially for the Valsugana production area.
For cases with similar P(M|{x i , y i }, I), additional criteria can be used to select the most plausible model. One of these is the Bayesian complexity C, which measures the number of model parameters that the data can support  [10]. This quantity is related to the gain of information (in the Shannon sense) and it is calculated from the maximum value and average value of the likelihood function. Accordingly to Occam's razor, for similar P(M|{x i , y i }, I) the simplest model has to be selected, i. e. , the model with the smallest C. If independent datasets are available, additional indications can be obtained by the compatibility of the probability distributions of the model parameters from the different measurements.
More information on the experimental setup can be found in a forthcoming paper  [15]  and in a similar previous experiment setup described in by the authors in  [16]. p 1/2 2s q-1 1/2 2p Six spectra are considered in the present article-one per ion type per spectrometer (referred here as inner and outer, indicating their position with respect to the storage ring). Each spectrum is obtained by several hours of data acquisition on the X-ray CCDs of the two spectrometers resulting in images such as in Figure  1  (left), here relative to a He-like uranium 1s 1/2 2p 3/2 → 1s 1/2 2s 1/2 intrashell transition. The corresponding slope correction and projection on the dispersion axis is presented in Figure  1  (right). The characteristics of the spectral line depends on the natural width of the transition and the setup of the experiment.
Moreover, for the present article on the model selection of spectral lines, no particular prior assumption on the type of profile is made. The 2D images are analysed using a recent version of the program Nested_fit (v4. 0), which is based on the nested sampling algorithm  [19] [20] [21] [22]. Due to the low count rate per channel (with typical value of 0-2 in the spectral line), Poisson statistics are considered for the likelihood to avoid the introduction of possible biases  [23, 24]. Two-dimensional data are modelled with functions with a form F[x, y] = f [x -x 0 (y)] + C with x 0 (y) = a + b(y -y 0 ), (3) where y 0 is the vertical coordinate of the middle of the detector and C is a constant modelling of a flat background. The evaluation of the Bayesian evidence was carried out with 2000 live points repeating the same evaluation eight times to estimate the evidence uncertainty.
The profile ERFPEAK has been taken as an arbitrary reference; only the evidence difference with respect to it is displayed. The super-Gaussian profile is favoured for most of the datasets, but not for the He-like and Li-like data of the inner spectrometer. For this reason, it has to be excluded in favour of the ERFPEAK profile which is the most or second most probable model in all datasets. Please note that a difference of 2 and 11 in the log value of the evidence corresponds to 2. 5 and 5 standard deviations and a p-value of 1% and 4 × 10 -7 in favour of the model with higher evidence. (A correspondence between evidence differences, sigmas and p-values can be found in  [25, 26] ). ERFPEAK is indeed the most likely profile a priori, which reflects the nature of the X-ray source obtained by the crossing of a Gaussian ion beam with a uniform cylindrical gas jet target  [17, 18].
These cases are known as not-in-lexicon/not-in-list (NIL) cases or out-of-KB. Cases where more than one standard entity is possible are referred to as multi-normalization. For instance, a mention such as "pasteurized milk cheeses" which may be normalized by concepts <cheese> and <pasteurized food> (sometimes called a multi-labeled mention), or an unprocessed composite mention such as "breast or ovarian cancer" which may be normalized by concepts <Breast Neoplasms> and <Ovarian Neoplasms>. The set of references often takes the form of an ontology or a knowledge base (or can often be related to it), and each standard entity is therefore a concept or an instance, that is a unique identifier and one or many associated textual labels (e. g. a preferred term, some synonyms). To standardize the vocabulary used in the remainder of this article, we mainly use the "ontology" (as set of references) and "concept" (as standard entity) terms, which fit easily with the addressed datasets.
Main difficulty: terminological variation EN in specialized domains is mainly concerned with the problem of terminological variations, that is, mentions with distinct surface forms that point to the same concept  [5, 6]. Moreover, a mention should be sometimes normalized by a concept whose no label matches this surface form  [7]. For instance, 60% of the mentions from the Bacteria Biotope dataset from the BioNLP 2013 Shared Task have a surface form different from the labels of the concept which should normalize them. The main source of variations are inflection (e. g. the mention "tortoises" which could be normalized by a concept <tor-toise>), synonymy (e. g. "chelonian"), hyponymy (e. g. "Testudo graeca"), nominal expansion (e. g. "georgia populations of gopher tortoises") and morphosyntactic variation (e.
This one enables method developers to estimate the generalization capacity of their method, while limiting overfitting on the test set  [24, 25]. To limit some dataset overfitting and thus have a fair estimation of how accurate a method is, k-fold cross-validation techniques have emerged. It mainly results in a separation into k folds of train(+ development) + test sets  [6, 26]. The final score of a method is then the average of the scores on the k sub-sets.
Evaluation measures The consensus evaluation metric used at the task level is the accuracy, which is basically the average of a strict metric over all evaluated mentions (see Eq. 1). More recently, less strict metrics have been introduced in normalization evaluations, such as top-k accuracy or similarity scores. Top-k accuracy (see Eq. 2) can be used when a method ranks at least k candidates for each mention, which is quite common with DistR-based methods. If the top-k accuracy of a method is high, a database curator could validate annotations more quickly by inspecting only k concept proposals (i. e. not all the concepts of the ontology), among which there is a great probability that the correct concept is. This might save time during database curation. where strict(m, c) = 1 if c is the correct concept of m, 0 otherwise.
Equation 1: General equation of accuracy. Here c i is the predicted concept for the mention m i , and N is the number of mentions in the dataset. where strict m, c 1 ,. , c k = 1 if one of the concepts in c 1 ,. , c k is the correct concept of m, 0 otherwise. Equation 2: General equation of accuracy. Here c 1 ,. , c k is the predicted k most ranked candidates. Also, a similarity score uses the ontological graph (when available) to calculate a nonstrict score by calculating a similarity distance between the predicted concept and the correct one. The objective is to evaluate the overall severity of errors, which means that a wrongly predicted concept may get some reward proportional to its distance to the correct concept. Wright  [27]  introduces the Lowest Common Ancestor distance and compares the result of their method with this score and the accuracy score.
Threats for validity Identifying biases in an evaluation protocol is a key path to meaningful comparisons of methods. In the following, we formulate a number of sources of biases we have identified and that we seek to illustrate. For this, we report several experiments we conducted to demonstrate the existence of these biases through two popular gold standards. We also used two state-of-the-art methods, and a custom baseline method. H1: uncertain method robustness on dataset variability It is expected that the performance of a method on a dataset is similar to that on any corpus of the same specific (1) Acc = 1 N • N i=1 strict(m i , c i ), (2) Acc@k = 1 N • N i=1 strict m i , c 1 i ,. , c k i , domain, type of texts and language. In practice however, small variations in experimental conditions often yield to drastic changes in performance.
While overclaiming is not the rule, some authors may be over optimistic about the generalization of their approach. For instance, in  [23] , the authors wrote "Our work is generally applicable to any type of entity", even if in most cases, authors tend to delimit the range of applicability of their method. For instance, some methods are assumed as ontology-specific, such as MetaMap: "[…] providing access to the concepts in the unified medical language system (UMLS) Metathesaurus from biomedical text"  [30]. Others delimit the type of text and the domain, such as in  [31] : "we deal with medical concept normalization in user generated texts". As a consequence, to estimate the robustness of their method, more and more authors evaluate it on several datasets. For instance, Sung et al. [23]  presents: "our model  […]  consistently outperforms previous state-of-the-art models almost reaching the upper bound on each dataset".
But even in that case, what do multiple evaluations demonstrate? If several gold standards share common features, a method could greatly perform on them, but not at all on distinct others. By the way, to our knowledge, there is no study on the feature similarities or dissimilarities of these used datasets, and their impact on robustness. And yet, it seems that the datasets from used batches often have relatively similar domains and/or types of documents. Moreover, datasets are often created with some simplifications compared to realistic normalization tasks. For instance, NIL and multi-normalization cases can be removed  [32]  or training examples can be adapted to better fit the associated test set, as the opposite is considered as a challenging task for ML approaches. H2: scoring metrics bias If there is no online evaluation platform or independent evaluation programs, authors compute the scores for their methods by themselves.