Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal 2-D $(n\times m,3,2,1)$-optical Orthogonal Codes

Optical orthogonal codes are commonly used as signature codes for optical code-division multiple access systems. So far, research on 2-D optical orthogonal codes has mainly concentrated on the same autocorrelation and cross-correlation constraints. In this paper, we are concerned about optimal 2-D optical orthogonal codes with the autocorrelation λ<i>a</i> and the cross-correlation 1. Some combinatorial constructions for 2-D (<i>n</i>×<i>m</i>,<i>k</i>,λ<i>a</i>,1) -optical orthogonal codes are presented. When <i>k</i>=3 and λ<i>a</i>=2, the exact number of codewords of an optimal 2-D (<i>n</i>×<i>m</i>,3,2,1)-optical orthogonal code is determined for any positive integers <i>n</i> ≡ 0,1,3,6,9,10 (mod 12) and <i>m</i> ≡ 2(mod 4).
ATTENTIVE RECURRENT COMPARATORS

Attentive Recurrent Comparators (ARCs) are a novel class of neural networks built with attention and recurrence that learn to estimate the similarity of a set of objects by cycling through them and making observations. The observations made in one object are conditioned on the observations made in all the other objects. This allows ARCs to learn to focus on the salient aspects needed to ascertain similarity. Our simplistic model that does not use any convolutions performs comparably to Deep Convolutional Siamese Networks on various visual tasks. However using ARCs and convolutional feature extractors in conjunction produces a model that is significantly better than any other method and has superior generalization capabilities. On the Omniglot dataset, ARC based models achieve an error rate of 1.5% in the One-Shot classification task a 2-3x reduction compared to the previous best models. This is also the first Deep Learning model to outperform humans (4.5%) and surpass the state of the art accuracy set by the highly specialized Hierarchical Bayesian Program Learning (HBPL) system (3.3%).
Appeared in ICDM ' 01 : The 2001 IEEE International Conference on Data Mining

We show that the e-commerce domain can provide all the right ingredients for successful data mining. We describe an integrated architecture for supporting this integration. The architecture can dramatically reduce the pre-processing, cleaning, and data understanding effort often documented to take 80% of the time in knowledge discovery projects. We emphasize the need for data collection at the application server layer (not the web server) in order to support logging of data and metadata that is essential to the discovery process. We describe the data transformation bridges required from the transaction processing systems and customer event streams (e.g., clickstreams) to the data warehouse. We detail the mining workbench, which needs to provide multiple views of the data through reporting, data mining algorithms, visualization, and OLAP. We conclude with a set of challenges. Note: A long version of this paper is also available [1].
Software Verification and Graph Similarity for Automated Evaluation of Students ’ Assignments I

Context: The number of students enrolled in universities at standard and on-line programming courses is rapidly increasing. This calls for automated evaluation of students assignments. Objective: We aim to develop methods and tools for objective and reliable automated grading that can also provide substantial and comprehensible feedback. Our approach targets introductory programming courses, which have a number of specific features and goals. The benefits are twofold: reducing the workload for teachers, and providing helpful feedback to students in the process of learning. Method: For sophisticated automated evaluation of students’ programs, our grading framework combines results of three approaches (i) testing, (ii) software verification, and (iii) control flow graph similarity measurement. We present our tools for software verification and control flow graph similarity measurement, which are publicly available and open source. The tools are based on an intermediate code representation, so they could be applied to a number of programming languages. This work was partially supported by the Serbian Ministry of Science grant 174021, by Swiss National Science Foundation grant SCOPES IZ73Z
Traffic Signs Detection and Recognition by Improved RBFNN

The paper develops radial basis function neural networks (RBFNN) applications in the traffic signs recognition. Firstly traffic signs are detected by using their color and shape informations. Then genetic algorithm (GA), which has a powerful global exploration capability, is applied to train RBFNN to obtain appropriate structures and parameters according to given objective functions. In order to improve recognition speed and accuracy, traffic signs are classified into three categories by special color and shape information. Three RBFNNs are designed for the three categories. Before fed into networks, the sign images are transformed into binary images and their features are optimized by linear discriminate analysis (LDA). The training set imitating possible sign transformations in real road conditions, is created to train and test the nets. The experimental results show the feasibility and validity of the proposed algorithm.
A Taxonomy on Big Data: Survey

The Big Data is the most popular paradigm nowadays and it has almost no untouched area. For instance, science, engineering, economics, business, social science, and government. The Big Data are used to boost up the organization performance using massive amount of dataset. The Data are assets of the organization, and these data gives revenue to the organizations. Therefore, the Big Data is spawning everywhere to enhance the organizations' revenue. Thus, many new technologies emerging based on Big Data. In this paper, we present the taxonomy of Big Data. Besides, we present in-depth insight on the Big Data paradigm.
Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.
BUILDING A SUSTAINABLE BUSINESS IN CHINA'S SMALL AND MEDIUM-SIZED ENTERPRISES (SMEs)

Given the SMEs a heterogeneous group in the Chinese society due to their inherent characteristics, this paper takes a preliminary step to look into the complicated and dynamic environment for engaging China's SMEs in corporate sustainability. It aims to provide insight into how China's SMEs perceive and practice sustainability, and to explore key drivers and barriers behind their environmental and social engagement. Findings reveal an apparent contradiction in the current status of China's SMEs towards sustainability: a high level of concern vs. a low level of engagement. The most important motivator appears to be improving corporate image, followed by governmental legislation. While barriers hindering SMEs' sustainable engagement vary, predominant problems involve a lack of awareness and perception, insufficient financial resources and insufficient or ill-suited external support. Accordingly, the paper highlights three areas for China's SMEs' further improvements: education, communication and cooperation.
Neural Rays for Occlusion-aware Image-based Rendering

any training on the scene. Then, the initialized NeuRay can be further optimized on every scene with little training timing to enforce spatial coherence to ensure view consistency in the presence of severe self-occlusion. Experiments demonstrate that NeuRay can quickly generate high-quality novel view images of unseen scenes with little finetuning and can handle complex scenes with severe self-occlusions which previous methods struggle with. 1 CCS Concepts: • Computing methodologies → Image-based rendering .
Hierarchical reinforcement learning in continuous state and multi-agent environments

This dissertation investigates the use of hierarchy and abstraction as a means of solving complex sequential decision making problems such as those with continuous state and/or continuous action spaces, and domains with multiple cooperative agents. This thesis develops several novel extensions to hierarchical reinforcement learning (HRL), and designs algorithms that are appropriate for such problems. It has been shown that the average reward optimality criterion is more natural than the more commonly used discounted criterion for continuing tasks. This thesis investigates two formulations of HRL based on the average reward semi-Markov decision process (SMDP) model, both for discrete-time and continuous-time. These formulations correspond to two notions of optimality that have been explored in previous work on HRL: hierarchical optimality and recursive optimality. Novel discrete-time and continuous-time algorithms, termed hierarchically optimal average reward RL (HAR) and recursively optimal average reward RL (RAR) are presented, which learn to find hierarchically and recursively optimal average reward policies. Two automated guided vehicle (AGV) scheduling problems are used as experimental testbeds to empirically study the performance of the proposed algorithms. Policy gradient reinforcement learning (PGRL) methods have several advantages over the more traditional value function RL algorithms in solving problems with continuous state spaces. However, they suffer from slow convergence. This thesis defines a family of hierarchical policy gradient RL (HPGRL) algorithms for scaling PGRL methods to high-dimensional domains. This thesis also examines the use of HRL to accelerate policy learning in cooperative multi-agent tasks. The use of hierarchy speeds up learning in multi-agent domains by making it possible to learn coordination skills at the level of subtasks instead of primitive actions. Subtask-level coordination allows for increased cooperation skills as agents do not get confused by low-level details. A framework for hierarchical multi-agent RL is developed and an algorithm called Cooperative HRL is presented that solves cooperative multi-agent problems more efficiently. (Abstract shortened by UMI.)
Imitrob: Imitation Learning Dataset for Training and Evaluating 6D Object Pose Estimators [Supplementary material

This supplementary material provides additional information to paper Imitrob: Imitation Learning Dataset for Training and Evaluating 6D Object Pose Estimators (DOI 10.1109/LRA.2023.3259735). Sec. A covers the setup details and additional experimental results, Sec. B contains links to the dataset documentation and supplementary code and describes the intended uses of the dataset, Sec. C provides details on the dataset licensing and hosting, including the main-tainance plan
DeepMDP: Learning Continuous Latent Space Models for Representation Learning

The first term of the RHS may be bounded above by KR }s1 ́ s2}2. For the second term of the RHS we apply the definition of the Wasserstein metric (Section 1). |Qps1, aq ́Qps2, aq| ď KRdSps1, s2q ` γKVW pPp ̈|s1, aq,Pp ̈|s2, aqq ď pKR ` γKVKPqdSps1, s2q ď pKR ` γKQKPqdSps1, s2q This recurrence has a finite fixed point given by KR 1 ́γKP . Thus the conditions of Lipschitz-Valued policies (Definition 3) are satisfied, completing the proof. Corollary 1. LetM be pKR,KP q-Lipschitz, then π ̊ is KR 1 ́γKP -Lipschitz-Valued. Proof. |V ps1q ́ V ps2q| “ |max a1PA Qps1, a1q ́max a2PA Qps2, a2q| ď max aPA |Qps1, aq ́Qps2, aq| Thus the condition for Lemma 1 holds and the result follows. A.2. Global DeepMDP Lemma 2. LetM and Ď M be an MDP and DeepMDP respectively, with an embedding function φ and global loss functions L8
U NIFIED -IO: A UNIFIED MODEL FOR VISION , LANGUAGE , AND MULTI - MODAL TASKS

We propose UNIFIED-IO, a model that performs a large variety of AI tasks spanning classical computer vision tasks, including pose estimation, object detection, depth estimation and image generation, vision-and-language tasks such as region captioning and referring expression comprehension, to natural language processing tasks such as question answering and paraphrasing. Developing a single unified model for such a large variety of tasks poses unique challenges due to the heterogeneous inputs and outputs pertaining to each task, including RGB images, perpixel maps, binary masks, bounding boxes, and language. We achieve this unification by homogenizing every supported input and output into a sequence of discrete vocabulary tokens. This common representation across all tasks allows us to train a single transformer-based architecture, jointly on over 80 diverse datasets in the vision and language fields. UNIFIED-IO is the first model capable of performing all 7 tasks on the GRIT benchmark and produces strong results across 16 diverse benchmarks like NYUv2-Depth, ImageNet, VQA2.0, OK-VQA, Swig, VizWizGround, BoolQ, and SciTail, with no task or benchmark specific fine-tuning. Code and demos for UNIFIED-IO are available at: unified-io.allenai.org ∗Equal contribution. Correspondence to jiasenl@allenai.org
An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs , and its Multicommodity Generalizations

In this paper we present an almost linear time algorithm for solving approximate maximum flow in undirected graphs. In particular, given a graph with m edges we show how to produce a 1−ε approximate maximum flow in time O(m · ε−2). Furthermore, we present this algorithm as part of a general framework that also allows us to achieve a running time of O(m1+o(1)ε−2k2) for the maximum concurrent k-commodity flow problem, the first such algorithm with an almost linear dependence on m. We also note that independently Jonah Sherman has produced an almost linear time algorithm for maximum flow and we thank him for coordinating submissions.
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptation Semantic Segmentation

Deep learning approaches have shown promising results in remote sensing high 1 spatial resolution (HSR) land-cover mapping. However, urban and rural scenes 2 can show completely different geographical landscapes, and the inadequate gener-3 alizability of these algorithms hinders city-level or national-level mapping. Most 4 of the existing HSR land-cover datasets only focus on improvement of the se-5 mantic segmentation in one domain (urban or rural), thereby ignoring the model 6 transferability. In this paper, we introduce the Land-cOVEr Domain Adaptation 7 semantic segmentation (LoveDA) dataset to promote large-scale land-cover map-8 ping. The LoveDA dataset contains 3338 aerial images with 86516 annotated 9 objects for seven common land-cover categories. Compared to the existing datasets, 10 the LoveDA dataset encompasses two domains (urban and rural), which brings 11 considerable challenges due to the: 1) multi-scale objects; 2) complex background 12 samples; and 3) inconsistent class distributions. The LoveDA dataset is suitable 13 for both land-cover semantic segmentation and unsupervised domain adaptation 14 (UDA) tasks. Accordingly, we benchmarked the LoveDA dataset on nine semantic 15 segmentation methods and eight UDA methods. Some exploratory studies were 16 also carried out to ﬁnd alternative ways to address these challenges. The code and 17 data will be available at: https://github.com/Junjue-Wang/LoveDA 18
Triptychon: Usability evaluation and implementation of a web-based application for patients’ lab and vital parameters

Background A major challenge in healthcare is the interpretation of the constantly increasing amount of clinical data of interest to inpatients for diagnosis and therapy. It is vital to accurately structure and represent data from different sources to help clinicians make informed decisions. Objective We evaluated the usability of our tool ‘Triptychon’ – a three-part visualisation dashboard of essential patients’ medical data provided by a direct overview of their hospitalisation information, laboratory, and vital parameters over time. Methods The study followed a cohort of 20 participants using the mixed-methods approach, including interviews and the usability questionnaires, Health Information Technology Usability Evaluation Scale (Health-ITUES), and User Experience Questionnaire (UEQ). The participant's interactions with the dashboard were also observed. A thematic analysis approach was applied to analyse qualitative data and the quantitative data's task completion time and success rates. Results The usability evaluation of the visualisation dashboard revealed issues relating to the terminology used in the user interface and colour coding in its left and middle panels. The Health-ITUES score was 3.72 (standard deviation (SD) = 1.0), and the UEQ score was 1.6 (SD = 0.74). The study demonstrated improvements in intuitive dashboard use and overall satisfaction with using the dashboard daily. Conclusion The Triptychon dashboard is a promising new tool for medical data presentation. We identified design and layout issues of the dashboard for improving its usability in routine clinical practice. According to users’ feedback, the three panels on the dashboard provided a holistic view of a patient's hospital stay.
Composite quality measures of abdominal surgery at a population level: systematic review

Abstract Background Measurement of surgical quality at a population level is challenging. Composite quality measures derived from administrative and clinical information systems could support system-wide surgical quality improvement by providing a simple metric that can be evaluated over time. The aim of this systematic review was to identify published studies of composite measures used to assess the overall quality of abdominal surgical services at a hospital or population level. Methods A search was conducted in PubMed and MEDLINE for references describing measurement instruments evaluating the overall quality of abdominal surgery. Instruments combining multiple process and quality indicators into a single composite quality score were included. The identified instruments were described in terms of transparency, justification, handling of missing data, case-mix adjustment, scale branding and choice of weight and uncertainty to assess their relative strengths and weaknesses (PROSPERO registration: CRD42022345074). Results Of 5234 manuscripts screened, 13 were included. Ten unique composite quality measures were identified, mostly developed within the past decade. Outcome measures such as mortality rate (40 per cent), length of stay (40 per cent), complication rate (60 per cent) and morbidity rate (70 per cent) were consistently included. A major challenge for all instruments is the reliance of valid administrative data and the challenges of assigning appropriate weights to the underlying instrument components. A conceptual framework for composite measures of surgical quality was developed. Conclusion None of the composite quality measures identified demonstrated marked superiority over others. The degree to which administrative and clinical data influences each composite measure differs in important ways. There is a need for further testing and development of these measures.
Fu’s subcutaneous needling as an adjunctive therapy for diaphragmatic dysfunction in a critically ill patient with severe neurologic disability: A case report

Rationale: Diaphragmatic dysfunction is prevalent among intensive care unit patients. The use of Fu’s subcutaneous needling (FSN) for respiratory problems is a new issue and few study has been conducted so far. Patient concerns: Despite conventional treatments, the patient continued using noninvasive ventilation after discharge from the intensive care unit due to diaphragmatic dysfunction. Diagnosis: Diaphragmatic dysfunction. Interventions: After the myofascial trigger points were confirmed in the neck, chest, and abdomen area, FSN therapy was performed using disposable FSN needles. FSN needles were penetrated into the subcutaneous layer. Outcomes: The patient dyspnea and tachypnea improved, and noninvasive ventilation time dropped significantly. The patient was successfully weaned from the ventilator after 3 sessions of FSN therapy, and there was an increase in diaphragmatic excursion and tidal fraction of the diaphragm via the ultrasound imaging. We found no evidence of relapse 12 months after treatment. Lessons: FSN therapy has potential as an alternative strategy for patients with diaphragmatic dysfunction and severe neurologic disabilities who do not respond well to conventional therapies, but further research is still required to establish the effects of FSN on diaphragmatic function.
Safety and effectiveness of endotracheal intubation in critically ill emergency patients with videolaryngoscopy

To investigate the safety and efficacy of video laryngoscopy for endotracheal intubation in critically ill patients. A total of 106 critically ill emergency patients treated at our hospital between January 2021 and June 2022 were randomly divided into 2 groups, the visual and direct groups, with 53 patients in each group. Both groups were treated with endotracheal intubation; the visual group was treated with video laryngoscopy, and the direct group was treated with conventional direct laryngoscopy. The Cormack–Lehane grade, percentage of glottic opening score, success rate of one intubation, intubation time, number of intubation attempts, hemodynamic values, and complications were compared between the 2 groups. The success rates of glottis exposure and one-time intubation were significantly higher while the intubation time and number of intubation attempts significantly lower in the visual group than in the direct group. The heart rate, mean arterial pressure (MAP), or blood oxygen saturation did not differ significantly between the 2 groups 10 minutes after entering the room (T0) or after anesthesia induction (T1). MAP was significantly lower in the visual group than in the direct group during immediate intubation (T2). The heart rate and MAP were significantly lower in the visual group than in the direct group 1 minute (T3) and 5 minutes (T4) after intubation. The incidences of intubation-related complications in the visual and direct groups were 7.55% and 22.60%, showing a significant difference. Endotracheal intubation under videolaryngoscopy is safer and more effective for critically ill emergency patients than conventional direct laryngoscopy. This can improve the success rate of intubation, reduce the intubation time, and reduce its effect on hemodynamics. Therefore, it is worthy of further clinical application.