dataesr/scientific-paragraphs-categorization huggingface dataset:
Strong Scaling: CT Scan of a Human Foot The foot dataset  [67]  was chosen to represent smaller data sizes with a resolution of 256 3 , simplified with a persistence threshold of 110. It consists of a CT scan of the tip of a foot, where the threshold of 110 was chosen to represent each bone with its own region. Regarding the total execution time at 24 Threads in Tab. 2, a speedup of over 50x and almost 40x (TTK and MSCEER) can be achieved using PLMSS. The resulting graphs in Fig. 11  also show clear improvements regarding parallel efficiency, as PLMSS (0, 59) still achieves good results that hint towards using even more cores, where TTK (0, 26) and MSCEER (0, 11) are already in a range where more cores do not strongly improve runtime performance and communication overhead takes over.
For future work, we are planning to improve the PLMSS in various ways. The load on each of the threads can be imbalanced when a particular thread receives a lot of triangles to generate. Here, a workload balance system could be introduced. To scale to even larger datasets, an MPI implementation will be provided to enable distributed memory execution. The knowledge gained from creating the segmentations will be implemented into the TTK MS complex implementation to improve its performance and useability. As shown in Sec. 6, a thorough comparison between MS complex implementations is out of the scope of this paper. Comparing all publicly available implementations and characterizing them in terms of input simplicial complex, handling of functions that are not Morse, available simplification models (pre-vs.
Blind and visually impaired people face different challenges when navigating indoors and outdoors. In this context, we suggest developing an obstacle detection system based on a modified YOLO v5 neural network architecture. The suggested system is capable of recognizing and locating a set of landmark indoor and outdoor objects that are extremely useful for Blind and Visually Impaired (BVI) navigation aids. Training and evaluation experiments were conducted using two datasets: the IODR dataset for indoor object detection and the MS COCO dataset for outdoor object detection. We used several optimization strategies, such as model width scaling, quantization, and channel pruning, to guarantee that the suggested work is implemented in embedded devices in a lightweight manner. The proposed system was successful in achieving results that were extremely competitive in terms of processing time as well as the precision of obstacle detection.
Recently, Deep Learning-based architecture has emerged to present a new set of effective solutions to solve different computer vision and Artificial Intelligence issues. It has been applied to different fields of research including pedestrian detection  [3] , fatigue detection  [4] , emotion recognition, indoor object identification  [5] , indoor scene recognition  [6] , road sign detection  [7] , and medical applications  [8] [9] [10]. This work suggests a lightweight obstacle detection system able to detect landmark objects in indoor and outdoor environments by using deep learning approaches. The suggested system gives BVI persons the ability to move freely in both indoor and outdoor locations more safely. We note that the developed system has been trained and evaluated using two datasets: IODR dataset for indoor object detection and MS COCO dataset for outdoor object detection.
The proposed obstacle detection system has demonstrated very competitive detection accuracies as well as a good time process. We used two model compression strategies to contribute to a lightweight implementation of the proposed work: channel pruning and posttraining quantization. After optimization techniques application, we succeeded to obtain a lighter version of the developed system that can fit the memory of embedded devices. The remainder of the paper is organized as follows: Section 2 will contain a review of related works. Section 3 will describe the proposed obstacle avoidance architecture and compression techniques used for a lightweight implementation on FPGAs. The results of experiments on two benchmark datasets (IODR and MS COCO) will be presented in Section 4. Section 5 provides conclusions.
-High valuable object classes for BVI assistance navigation. -Training and evaluation processes have been conducted under very challenging conditions. -The proposed system has been trained and tested using two datasets: IODR  [24]  and MS COCO  [25]. -The obstacle detection system can detect a set of new landmark objects that were not considered in the state-of-the-art works. Deep learning architectures have advanced significantly in recent years, making them a primary solution for unsolvable tasks. Visual impairments and blindness can lead to social isolation. To solve this problem, we suggest building an obstacle detection system to help BVI people integrate into daily life and make life easier. The system being suggested is built using the one-stage neural network "YOLO v5". YOLO v5 architecture is very similar to YOLO v4 architecture but with some minor modifications.
Experiments for training and testing Obstacle detection presents one of the most active fields of research especially when it is applied to assistance navigation for blind and sighted persons. It is extremely important to correctly detect and localize objects for this category of persons to fully help them during their navigation in new indoor and outdoor unfamiliar environments. The proposed experiments were carried out using a desktop computer with an Intel i7 CPU, 32 GB of RAM, and an NVIDIA GTX 960 GPU with 4 GB of graphic memory. For the development of the proposed obstacle detection system, the PyTorch Deep Learning framework has been used with CUDA support and cuDNN library support. For image processing and manipulation, the open computer vision OpenCV library has been used. To ensure a robust system that can detect the maximum of possible objects that can face the BVI person during its navigation, experiments for training and testing were performed using two datasets: Indoor Objects Detection and Recognition dataset (IODR), which was used to  detect 21 landmark indoor objects, and MS COCO dataset, which was used to detect a set of other indoor and outdoor objects.
We note that we select from the MS COCO dataset only the valuable objects that can present obstacles to be avoided during BVI navigation. Training and testing experiments were conducted using images that were captured under challenging conditions, including a variety of lighting conditions, occlusion, different perspectives of objects, and high variations within and between classes which can highly contribute to building a robust obstacle detection system with high detection performances. The model has been trained for epochs, each epoch with 10,000 iterations. The value has been 0. 001. We address in this paper the obstacles and dangers avoidance to reach safely and independently their desired destinations. The selected objects used to build the proposed system are essential for enhancing the quality of life for individuals with visual impairments and fully assist them to more explore their indoor and outdoor surroundings.
The proposed obstacle detection system presents one step further to ensure a high safety degree for the BVI around the world by taking advantage of Deep Learning-based architectures. During the training and testing experiments, we only choose to use some specific object classes that are highly valuable to assist BVI to detect obstacles and to ensure more safety when accessing new unfamiliar environments. The IODR dataset provides 8000 indoor images containing 16 different indoor landmark objects and the MS COCO dataset provides 91generic objects and 328. 000 images. During the proposed experiments, we only focus our attention on 40 object classes that can contribute to the BVI assistance navigation and obstacle detection. In order to ensure a lightweight implementation of the proposed work, we applied different optimization techniques including channel pruning and quantization.
We note that based on Table  1  the proposed system is suitable to fit the memory of FPGAs or low-end devices. The proposed compression technique has reduced the number of parameters from 2. 1 million to 1. 7 million. Besides, the number of FLOPs was reduced from 4. 8 to 4. 2 which will accelerate the processing speed. Based on the achieved results, the proposed model presents high applicability in low-end devices with limited computation resources. The compression technique enhanced the processing speed but degraded the detection accuracy. The achieved results will be discussed in detail in the following. Table  2  provides the obtained per-class average precision of YOLO v5 before optimization techniques are applied in the IODR + MS COCO datasets. As provided in Table  2 , we obtained very encouraging detection precision in the objects selected from IODR and MS COCO datasets for almost all the class objects.
We note that these detection results are achieved only by applying the proposed optimization of the model width scaling. We achieved more than 80% as a per-class average precision for almost all the object classes and we achieved 81. 02% as mean Average Precision (mAP) for all the object classes. Table  3  reports the per-class detection precision on the IODR + MS COCO datasets after compression and optimization techniques application. As reported in Table  3 , we obtained very encouraging detection results despite the optimization techniques application. We succeeded to obtain very competitive detection accuracies for almost all the object classes that can highly contribute to increasing the safety level of indoor and outdoor assistance navigation for BVI persons by detecting different obstacles in a very precise way to save this category of persons from dangerous situations.
Study of ablation In this section, an ablation study is proposed to evaluate the effectiveness of the proposed work contribution. To contribute to an obstacle detection system deployable in embedded systems, we performed a width scaling method. We maintained the same model depth as in the YOLO v5s version and we manipulated the model width using different values. We used different model widths which refer to the number of the model's output channels. We performed different experiments to achieve a better model width that fits the proposed work requirements. We adopted different model width values including 0. 45, 0. 40, 0. 35 and 0. 30. We note that for every used value of the model width, we performed new training and testing of the model using the 40 object classes chosen from the two datasets: IODR and MS COCO.
Conclusion In this paper, we proposed a new assistive technology system that can help Blind and Visually Impaired (BVI) people navigate indoor and outdoor environments more safely. This system can be used to detect obstacles and help the user move around more confidently. Two datasets for training and testing are used to evaluate our obstacle detection system. In fact. the IODR dataset is used for indoor obstacle detection, and the MS COCO dataset is employed for outdoor obstacle detection. The proposed system is robust enough since it was trained and tested under extremely difficult situations to deal with real-world scenarios, particularly during BVI people's navigation in unknown locations. It was developed based on the one-stage neural network YOLO v5. Our system enables BVI persons to recognize a set of vital landmark objects highly valuable to assist them in autonomously navigating and exploring new unfamiliar environments.
Implementing Quantum Games in a Noisy Quantum Computer Two basic methods of implementation for the given games and scenarios will be used -the unitary transform and the qubit delegation methods. Each will be outlined in this section, with the advantages and disadvantages of each provided. We will use the circuit formalism to present our different implementations. All our codes and results can be found on https://quantcert. github. io/quantum game and are implemented using Qiskit  [17]  the open-source software that allows to run programs on the IBM Quantum Experience  [12]. In both scenarios Alice and Bob share the entangled states |ψ AB ⟩ of Eq (2). In the circuit formalism that quantum state is implemented by:  We will first outline the different implementation methods in general, and then specifically for each of the game scenarios in question.
Acknowledgment This work is supported by the  Graduate school EIPHI  (contract  ANR-17-EURE-0002 ) through the project  TACTICQ. We acknowledge the use of the  IBM Quantum Experience  and the  IBMQ-research program. The views expressed are those of the authors and do not reflect the official policy or position of IBM or the IBM Quantum Experience team. One would like to thank the developers of the open-source framework Qiskit.
When the calculated number of ports n does not correspond to any manufactured switch, we used as follows the nearest switches able to satisfy the network topology, while keeping unused links. Let's define n 1 (respectively n 2 ) so that n 1 (respectively n 2 ) is the number of hardware ports immediately smaller (respectively greater) than n. For instance n 1 = 12, n 2 = 16 and n = 13 means that no switch of 13 ports exists but we can find switches of 12 and 16 ports in the Cisco catalog. The power consumption when using n ports is evaluated by a linear regression between the two closest existing switches as expressed in Equation  5 where P (x) is the power consumption of a x-ports switch. P (n) = P (n 1 ) + P (n 2 )-P (n 1 ) n 2 -n 1 × (n -n 1 ) (5) As for considering the infrastructure, Power Usage Effectiveness (PUE) is the metric used to account for the energy consumption of the other equipment available in the data center such as the cooling system, the facilities lighting, etc.
Noting these gaps in extant literature, we seek to define explainable AI for OR (XAIOR) by a framework which we define in Section 2, where explainable AI is a must-have besides other forms of analytics like performance and responsible analytics when successfully developing and deploying advanced analytics that turn data into insights for improved managerial decision-making. This paper thus takes a broader stance than solely looking at explainable AI but also reviews the most important aspects of performance and responsible analytics. We answer the following four broad questions related to explainable AI for OR (XAIOR) in subsequent sections: • What is XAIOR? In Section 2, we provide a domain-specific definition of XAIOR and introduce a comprehensive, normative framework of XAIOR and its three dimensions: performance analytics (PA), attributable analytics (AA), and responsible analytics (RA).
• How should XAIOR be implemented? We present a non-exhaustive overview of critical XAIOR methodologies in Section 3, including experimental design and data selection, feature engineering and data preparation, algorithmic design and choice, post-hoc interpretation methods, and evaluation strategies and metrics. • Where should XAIOR be deployed? In Section 4, we outline key applications of XAIOR, focusing on important OR domains such as forecasting, risk analysis, inventory control, marketing, and supply chain management. • What is the future of XAIOR? We develop an agenda for further research in Section 5.
Defining Explainable AI for Operational Research We define XAIOR as the conceptualization and application of advanced methods for transforming data into insights that are simultaneously performant, attributable, and responsible for solving OR problems and enhancing decision-making. This definition underlies a more elaborate framework of XAIOR, as presented in Figure  2. The framework comprises three dimensions, reflecting the three overarching principles that guide the conceptualization of XAIOR. They explain the inner workings of analytical methods and the reasons for any proposed decisions. These three dimensions align with the three types of analytics, as we explain next. 1. Performance analytics (PA). In the XAIOR framework, the final solution can make valid, reliable decisions in a scalable manner.
B laszczyński et al. (  2021 ) also derives monotonic decision rules from bank data, seeking to explain fraudulent behaviors by customers in a way that makes sense to lenders. • Actionability implies that a method can pinpoint, for the decision-maker, how and where to allocate resources to solve the problem. For instance, da  Costa et al. (2023)  propose a dynamic traveling maintainer problem with alerts that always approximates the optimal policy to act upon when given access to complete condition information to avoid downtime of industrial assets. Another example,  Baykasoǧlu and Özbakir (2007)  proposes MEPARminer, a multi-expression program for association rule mining, that can discover effective and actionable IF-THEN classification rules, which in turn improve decision accuracy while also giving problem domain experts a helpful means to extract knowledge from the data and take related action.
The popular feature engineering method TF-IDF counts the frequency of words in a text; word embedding models transform raw text into dense real-valued vectors while encoding the meaning of the words (e. g. , word2vec). Powerful language models such as BERT can even gauge customer satisfaction expressed in text samples  (Aldunate et al. , 2022). Network data also can be automatically feature-engineered to extract structural information about observations in the network in relation to neighbors and positions based on learning of the node embeddings with neural network-based methods, such as node2vec and graphSAGE  ( Óskarsdóttir et al. , 2020; Van Belle et al. , 2022). • Hybrid feature engineering methods combine manual and automated feature engineering. Gosiewska et al. (2021)  propose SAFE ML: A complex supervisor model that engineers interpretable features that subsequently get added to an easily interpretable model.
Global explanation methods Global explanation methods explain a model's prediction over an entire data set. A commonly used approach is to look at what features are most 'important' for the model prediction. Breiman (2001)  arguably first popularized these permutation-based feature importance scores, or simply permutation importances (PI), in his seminal paper on random forests. Randomly changing a feature's value across the entire dataset and assessing the impact on the model's predictive accuracy gives an indication of how important that feature is to the prediction. Local methods such as SHAP can also be used as global feature importance methods, by averaging instance-level values over the data set at hand. Related methods proposed for global feature importance analysis are Sobol indices and Shapley effects.
Finally, as to RA, evaluation metrics may be applied to assess the privacy of the dataset, in terms of k-anonymity, l-diversity or t-closeness, or the fairness to sensitive groups using measures such as statistical parity, or of the model with metrics such as demographic parity or equalized opportunity  (Martens, 2022, pp. 175-176). Additionally, robustness and sustainability may be assessed quantitatively, although no agreement exists in the literature on standard evaluation approaches and metrics. Prior to deployment, in addition to adopting commonly used methods to simulate the future performance of the solution, such as out-of-sample evaluation or cross-validation, a small-scale field test or experiment, such as an A/B test, may be set up to assess realworld performance. Once an analytical solution is deployed, the operational performance typically needs to be monitored continuously.
Marketing The marketing field analyses customer data to describe and predict customer behavior in various stages of the customer journey, i. e. , the acquisition, development with cross-and upselling activities, and retention stage. This section highlights noteworthy research in these areas across the three dimensions of the XAIOR framework. • Performance analytics. Two important business characteristics explain the prevalence of PA in marketing. 1. The evolutive nature of business contexts. For instance, marketing budgets and target class distributions may vary over time. E. g. , digital ad targeting is a function of evolving factors such as product lifecycle stage, available budget, expected conversion rate, etc. Such changing contexts imply that the decision threshold to target someone with an ad also will vary.
The marketing field has focused heavily on making customer analytics models attributable. First, global explanation methods like rule extraction were proposed previously for churn prediction and response modeling  (Verbeke et al. , 2017). Furthermore, there is a stream of hybrid modeling approaches where homogeneous segments are first identified in the customer base. Subsequently, segment-specific models are trained. This approach was found to enhance both predictive performance and understandability. For instance, Table  6  visualizes the logit leaf model (LLM) approach proposed by  De Caigny et al. (2018)  on the publicly available cell2cell customer churn prediction dataset. The LLM consists of two steps. In the first step, customer segments are identified using decision rules, and in the second phase, a logistic regression model is created for every leaf of this tree.
Other applications In this section, we summarize other relatively less-known applications of analytics as part of OR. We particularly focus on the following OR domains: healthcare, litigation, and educational analytics, and discuss their link with the building blocks of the XAIOR framework. • Healthcare. Various applications have focused on optimizing the decision-making strategy in the context of improving the health and well-being of people, such as in the context of organ transplantation operations. The use of data analytics methods, as opposed to intuition and experience-based utility functions, for optimal allocation of the organs to potential recipients, optimizes the allocation process and thereby saves more lives  (Al-Ebbini et al. , 2017). Not only do analytics predict the prognostics of these significant events, but also explain the reasoning behind the prescribed actions.
AA is used synergistically to develop and deploy powerful mathematical models as screening mechanisms for the future onset of diabetes complications. For instance, machine learning models developed on the electronic health records (EHR) database are used to predict diabetic retinopathy  (Piri et al. , 2017) , a leading cause of blindness among working-aged adults. Such an analytics model is used as a screening tool by medical professionals to urge diabetic patients to get it confirmed and treated so that they maintain their eyesight. Such automated early warning mechanisms are especially useful in rural settings where specialist like ophthalmologists is scarce  (Wang et al. , 2021). Another healthcare domain where the use of an EHR database along with AA makes a significant impact is in the analyses of relatively rare chronic diseases  (Reddy and Delen, 2018).
For each of these themes, highlighted with a specific icon, we list some exemplary research questions in Figure  7  that will further inspire readers. • Data innovation. Data enrichment and data augmentation studies are important in OR, showing the importance of innovative, unstructured, or structured data sources, such as textual, image, or social network data, to improve models. In the near future, new data sources such as data issued by generative AI models, geospatial data, data linked to IoT applications, or data from the Metaverse might show value for various domains. Despite the importance of data augmentation studies, they traditionally focus mainly on the PA dimension of the XAIOR framework. It is, however, relevant to link new data sources across all dimensions of the XAIOR framework.