cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Approximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planning
Creators: name: Acar, Cihan
name: Tee, Keng Peng
Publisher: name: arXiv
Year: 2022
Subjects: subject: Robotics (cs.RO)
subject: Artificial Intelligence (cs.AI)
subject: FOS: Computer and information sciences

description: Sampling-based motion planning under task constraints is challenging because the null-measure constraint manifold in the configuration space makes rejection sampling extremely inefficient, if not impossible. This paper presents a learning-based sampling strategy for constrained motion planning problems. We investigate the use of two well-known deep generative models, the Conditional Variational Autoencoder (CVAE) and the Conditional Generative Adversarial Net (CGAN), to generate constraint-satisfying sample configurations. Instead of precomputed graphs, we use generative models conditioned on constraint parameters for approximating the constraint manifold. This approach allows for the efficient drawing of constraint-satisfying samples online without any need for modification of available sampling-based motion planning algorithms. We evaluate the efficiency of these two generative models in terms of their sampling accuracy and coverage of sampling distribution. Simulations and experiments are also conducted for different constraint tasks on two robotic platforms.
Dessins et journaux réalisés par d'anciens volontaires des Brigades internationales internés en France_3
Creators: name: Marty, André
Publisher: name: NAKALA - https://nakala.fr (Huma-Num - CNRS)
publisherIdentifier: https://ror.org/04ces3204
publisherIdentifierScheme: ROR
Year: 2015

description: Brochures politiques réalisées dans les camps d'internements dans plusieurs langues (polonais, chinois, allemand etc.)
Additional file 2 of Oropharynx and hyoid bone changes in female extraction patients with distinct sagittal and vertical skeletal patterns: a retrospective study
Creators: name: Guo, Runzhi
name: Wang, Shuo
name: Zhang, Liwen
name: Li, Linwei
name: Yu, Qianyao
name: Huang, Yiping
name: Li, Weiran
Publisher: name: figshare
Year: 2022
Subjects: subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Physiology
subject: Biotechnology
subject: Environmental Sciences not elsewhere classified
subject: Ecology
subject: Sociology
subject: FOS: Sociology
subject: Biological Sciences not elsewhere classified
subject: Cancer
subject: Science Policy
subject: Plant Biology

description: Additional file 2: Supplementary Table 2. Comparison of the changes in oropharynx and hyoid bone position between class II-norm extraction patients and class II-norm non-extraction patients.
A Novel Approach for Cancellation of Non-Aligned Inter Spreading Factor Interference in LoRa Systems
Creators: name: Zhang, Qiaohan
name: Bizon, Ivo
name: Kumar, Atul
name: Martinez, Ana Belen
name: Chafii, Marwa
name: Fettweis, Gerhard
Publisher: name: arXiv
Year: 2022
Subjects: subject: Signal Processing (eess.SP)
subject: FOS: Electrical engineering, electronic engineering, information engineering

description: Long Range (LoRa) has become a key enabler technology for low power wide area networks. However, due to its ALOHA-based medium access scheme, LoRa has to cope with collisions that limit the capacity and network scalability. Collisions between randomly overlapped signals modulated with different spreading factors (SFs) result in inter-SF interference, which increases the packet loss likelihood when signal-to-interference ratio (SIR) is low. This issue cannot be resolved by channel coding since the probability of error distance is not concentrated around the adjacent symbol. In this paper, we analytically model this interference, and propose an interference cancellation method based on the idea of segmentation of the received signal. This scheme has three steps. First, the SF of the interference signal is identified, then the equivalent data symbol and complex amplitude of the interference are estimated. Finally, the estimated interference signal is subtracted from the received signal before demodulation. Unlike conventional serial interference cancellation (SIC), this scheme can directly estimate and reconstruct the non-aligned inter-SF interference without synchronization. Simulation results show that the proposed method can significantly reduce the symbol error rate (SER) under low SIR compared with the conventional demodulation. Moreover, it also shows high robustness to fractional sample timing offset (STO) and carrier frequency offset (CFO) of interference. The presented results clearly show the effectiveness of the proposed method in terms of the SER performance.
description: Published in: IEEE Open Journal of the Communications Society (Early Access)
title: 02_1889
Creators: name: Wesley Baldwin, John
name: Baldwin, John W.
Publisher: name: NAKALA - https://nakala.fr (Huma-Num - CNRS)
publisherIdentifier: https://ror.org/04ces3204
publisherIdentifierScheme: ROR
Year: 2018
Subjects: subject: LaMOP
subject: Laboratoire de Médiévistique Occidentale de Paris

description: L'ensemble de ces notes ont été crée par John Baldwin lors d'une de ses premières périodes de résidence à Paris. Ces documents comprennent tous les magistri qu’il avait pu rencontrer dans ses sources. Aux XIIème et XIIIème siècles, les universités n'existant pas encore, le titre de magister s'il évoque certainement une compétence n'est pas rattaché de façon systématique au passage par les écoles. De surcroît, les magistris sont le plus souvent désignés par leur seul prénom. La lecture de ces données est donc difficile et suppose un long travail pour tenter d'identifier un individu auquel on pourrait rattacher plusieurs mentions. John Baldwin avait donc renoncé à utiliser ces données pour donner un prolongement à sa magnifique étude sur les gradués d'université et leur emploi par les Capétiens et les Plantagenêts. Néanmoins, il a conservé ces données et elles sont potentiellement très utile dans le cadre de Studium, comme le sont toutes les mentions dans les volumes des Fasti Ecclesiae Gallicanae de chanoines portant ce titre de magister sans autres précisions. L'ampleur des dépouillements rend ces données très utiles et nous devons une grande reconnaissance à John Baldwin pour avoir pris la peine de faire photocopier ses fiches et de nous les avoir transmises amicalement.
Les données ainsi que les images des fiches sont accessibles sur le site Internet à l'adresse suivante : http://studium-baldwin.lamop.fr/.
Les transcriptions des fiches ont été effectuées par des étudiants de Paris 1 Panthéon - Sorbonne mais elles n'ont pas été corrigées par la suite.
description: Relation: https://lamop.hypotheses.org/
Additional file 3 of Oropharynx and hyoid bone changes in female extraction patients with distinct sagittal and vertical skeletal patterns: a retrospective study
Creators: name: Guo, Runzhi
name: Wang, Shuo
name: Zhang, Liwen
name: Li, Linwei
name: Yu, Qianyao
name: Huang, Yiping
name: Li, Weiran
Publisher: name: figshare
Year: 2022
Subjects: subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Physiology
subject: Biotechnology
subject: Environmental Sciences not elsewhere classified
subject: Ecology
subject: Sociology
subject: FOS: Sociology
subject: Biological Sciences not elsewhere classified
subject: Cancer
subject: Science Policy
subject: Plant Biology

description: Additional file 3: Supplementary Table 3. Comparison of the changes in oropharynx and hyoid bone position between class I-hyper extraction patients and class I- hyper non-extraction patients.
Uplifting Interviews in Social Science with Individual Data Visualization: the case of Music Listening
Creators: name: Cura, Robin
name: Beaumont, Amélie
name: Beuscart, Jean-Samuel
name: Coavoux, Samuel
name: de Fozières, Noé Latreille
name: Bigot, Brenda Le
name: Renisio, Yann
name: Moussallam, Manuel
name: Louail, Thomas
Publisher: name: arXiv
Year: 2022
Subjects: subject: Human-Computer Interaction (cs.HC)
subject: FOS: Computer and information sciences

description: Collecting accurate and fine-grain information about the music people like, dislike and actually listen to has long been a challenge for sociologists. As millions of people now use online music streaming services, research can build upon the individual listening history data that are collected by these platforms. Individual interviews in particular can benefit from such data, by allowing the interviewers to immerse themselves in the musical universe of consenting respondents, and thus ask them contextualized questions and get more precise answers. Designing a visual exploration tool allowing such an immersion is however difficult, because of the volume and heterogeneity of the listening data, the unequal "visual literacy" of the prospective users, or the interviewers' potential lack of knowledge of the music listened to by the respondents. In this case study we discuss the design and evaluation of such a tool. Designed with social scientists, its purpose is to help them in preparing and conducting semi-structured interviews that address various aspects of the listening experience. It was evaluated during thirty interviews with consenting users of a streaming platform in France.
description: Preprint of the paper to be presented at ACM CHI Conference on Human Factors in Computing Systems (CHI'22), Case Studies track, 10 pages, 5 figures
Additional file 4 of Oropharynx and hyoid bone changes in female extraction patients with distinct sagittal and vertical skeletal patterns: a retrospective study
Creators: name: Guo, Runzhi
name: Wang, Shuo
name: Zhang, Liwen
name: Li, Linwei
name: Yu, Qianyao
name: Huang, Yiping
name: Li, Weiran
Publisher: name: figshare
Year: 2022
Subjects: subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Physiology
subject: Biotechnology
subject: Environmental Sciences not elsewhere classified
subject: Ecology
subject: Sociology
subject: FOS: Sociology
subject: Biological Sciences not elsewhere classified
subject: Cancer
subject: Science Policy
subject: Plant Biology

description: Additional file 4: Supplementary Table 4. Comparison of the changes in oropharynx and hyoid bone position between class II-hyper extraction patients and class II- hyper non-extraction patients.
INGLIZ, O'ZBEK VA RUS TILLARIDA TURISTIK TERMINLARDAN FOYDALANISH USULLARI
Creators: name: Imamova, Gulchexra
Publisher: name: Zenodo
Year: 2024

description: Tilshunoslikdа, jumlаdаn, o‘zbek tilshunosligidа qo‘shmа so‘zlаr vа so‘z birikmаlаrini fаrqlаsh, qаt’iy chegаrаlаsh аnchа murаkkаb vа hаl etilishi lozim bo‘lgаn mаsаlаlаrdаn biri, deyish mumkin. Qo‘shmа so‘z vа so‘z birikmаlаrining chegаrаsi mаsаlаsini hаr bir tilning xаrаkterli fono-morfologik vа sintаktik me’yorlаrgа аsoslаngаn, аniq mezongа tаyanib belgilаsh tаlаb etilаdi. Bu mаsаlа fаqаt o‘zbek tilshunosligidа emаs, rus tilshunosligidа hаm hozirgаchа kаm o‘rgаnilgаn vа bаhsli mаsаlаlаrdаn bo‘lib kelmoqdа.
Additional file 1 of Gastrointestinal dysfunction is associated with mortality in severe burn patients: a 10-year retrospective observational study from South China
Creators: name: He, Qiu-Lan
name: Gao, Shao-Wei
name: Qin, Ying
name: Huang, Run-Cheng
name: Chen, Cai-Yun
name: Zhou, Fei
name: Lin, Hong-Cheng
name: Huang, Wen-Qi
Publisher: name: figshare
Year: 2022
Subjects: subject: Medicine
subject: Microbiology
subject: FOS: Biological sciences
subject: Cell Biology
subject: Genetics
subject: Molecular Biology
subject: Neuroscience
subject: Biotechnology
subject: Ecology
subject: Developmental Biology
subject: Science Policy
subject: Mental Health

description: Additional file 1: Fig. S1. Endoscopy images of gastric haemorrhage/ulcer in patients with extensive burns. Fig. S2. Kaplan–Meier survival curves: exclusive GI haemorrhage/ulcer (without a motility disorder) vs. GI motility disorder (constipation/diarrhoea, nausea/vomiting, or abdominal distension).
GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with Application to Robotic Grasping
Creators: name: Kurkcu, Anil
name: Acar, Cihan
name: Campolo, Domenico
name: Tee, Keng Peng
Publisher: name: arXiv
Year: 2022
Subjects: subject: Robotics (cs.RO)
subject: Artificial Intelligence (cs.AI)
subject: FOS: Computer and information sciences

description: The domain of robotics is challenging to apply deep reinforcement learning due to the need for large amounts of data and for ensuring safety during learning. Curriculum learning has shown good performance in terms of sample- efficient deep learning. In this paper, we propose an algorithm (named GloCAL) that creates a curriculum for an agent to learn multiple discrete tasks, based on clustering tasks according to their evaluation scores. From the highest-performing cluster, a global task representative of the cluster is identified for learning a global policy that transfers to subsequently formed new clusters, while the remaining tasks in the cluster are learned as local policies. The efficacy and efficiency of our GloCAL algorithm are compared with other approaches in the domain of grasp learning for 49 objects with varied object complexity and grasp difficulty from the EGAD! dataset. The results show that GloCAL is able to learn to grasp 100% of the objects, whereas other approaches achieve at most 86% despite being given 1.5 times longer training time.
OmniPD: One-Step Person Detection in Top-View Omnidirectional Indoor Scenes
Creators: name: Yu, Jingrui
name: Seidel, Roman
name: Hirtz, Gangolf
Publisher: name: arXiv
Year: 2022
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: Artificial Intelligence (cs.AI)
subject: FOS: Computer and information sciences

description: We propose a one-step person detector for topview omnidirectional indoor scenes based on convolutional neural networks (CNNs). While state of the art person detectors reach competitive results on perspective images, missing CNN architectures as well as training data that follows the distortion of omnidirectional images makes current approaches not applicable to our data. The method predicts bounding boxes of multiple persons directly in omnidirectional images without perspective transformation, which reduces overhead of pre- and post-processing and enables real-time performance. The basic idea is to utilize transfer learning to fine-tune CNNs trained on perspective images with data augmentation techniques for detection in omnidirectional images. We fine-tune two variants of Single Shot MultiBox detectors (SSDs). The first one uses Mobilenet v1 FPN as feature extractor (moSSD). The second one uses ResNet50 v1 FPN (resSSD). Both models are pre-trained on Microsoft Common Objects in Context (COCO) dataset. We fine-tune both models on PASCAL VOC07 and VOC12 datasets, specifically on class person. Random 90-degree rotation and random vertical flipping are used for data augmentation in addition to the methods proposed by original SSD. We reach an average precision (AP) of 67.3 % with moSSD and 74.9 % with resSSD onthe evaluation dataset. To enhance the fine-tuning process, we add a subset of HDA Person dataset and a subset of PIROPOdatabase and reduce the number of perspective images to PASCAL VOC07. The AP rises to 83.2 % for moSSD and 86.3 % for resSSD, respectively. The average inference speed is 28 ms per image for moSSD and 38 ms per image for resSSD using Nvidia Quadro P6000. Our method is applicable to other CNN-based object detectors and can potentially generalize for detecting other objects in omnidirectional images.
title: Candidature projet EXPLOCROIRE 2017
Creators: name: IDEMEC (UMR 7307)
Publisher: name: NAKALA - https://nakala.fr (Huma-Num - CNRS)
publisherIdentifier: https://ror.org/04ces3204
publisherIdentifierScheme: ROR
Year: 2019

description: Projet EXPLOCROIRE Explorer la Subjectivation dans le croire, porté par Christophe Pons (IDEMEC), en 2017
(Fractional) Online Stochastic Matching via Fine-Grained Offline Statistics
Creators: name: Tang, Zhihao Gavin
name: Wu, Hongxun
name: Wu, Jinzhao
Publisher: name: arXiv
Year: 2022
Subjects: subject: Data Structures and Algorithms (cs.DS)
subject: FOS: Computer and information sciences

description: Motivated by display advertising on the internet, the online stochastic matching problem is proposed by Feldman, Mehta, Mirrokni, and Muthukrishnan (FOCS 2009). Consider a stochastic bipartite graph with offline vertices on one side and with i.i.d. online vertices on the other side. The algorithm knows the offline vertices and the distribution of the online vertices in advance. Upon the arrival of each online vertex, its type is realized and the algorithm immediately and irrevocably decides how to match it. In the vertex-weighted version of the problem, each offline vertex is associated with a weight and the goal is to maximize the total weight of the matching. In this paper, we generalize the model to allow non-identical online vertices and focus on the fractional version of the vertex-weighted stochastic matching. We design fractional algorithms that are $0.718$-competitive and $0.731$-competitive for non i.i.d. arrivals and i.i.d. arrivals respectively. We also prove that no fractional algorithm can achieve a competitive ratio better than $0.75$ for non i.i.d. arrivals. Furthermore, we round our fractional algorithms by applying the recently developed multiway online correlated selection by Gao et al. (FOCS 2021) and achieve $0.666$-competitive and $0.704$-competitive integral algorithms for non i.i.d. arrivals and i.i.d. arrivals. Our results for non i.i.d. arrivals are the first algorithms beating the $1-1/e \approx 0.632$ barrier of the classical adversarial setting. Our $0.704$-competitive integral algorithm for i.i.d. arrivals slightly improves the state-of-the-art $0.701$-competitive ratio by Huang and Shu (STOC 2021).
description: To appear in STOC 2022
Additional file 2 of Gastrointestinal dysfunction is associated with mortality in severe burn patients: a 10-year retrospective observational study from South China
Creators: name: He, Qiu-Lan
name: Gao, Shao-Wei
name: Qin, Ying
name: Huang, Run-Cheng
name: Chen, Cai-Yun
name: Zhou, Fei
name: Lin, Hong-Cheng
name: Huang, Wen-Qi
Publisher: name: figshare
Year: 2022
Subjects: subject: Medicine
subject: Microbiology
subject: FOS: Biological sciences
subject: Cell Biology
subject: Genetics
subject: Molecular Biology
subject: Neuroscience
subject: Biotechnology
subject: Ecology
subject: Developmental Biology
subject: Science Policy
subject: Mental Health

description: Additional file 2: Table S1. Factors associated with GI dysfunction in patients with severe burns (n = 328). Table S2. Correlations between GI dysfunction and discrete clinical variables (n = 149). Table S3. Factors associated with 90-day mortality in patients with severe burns (n = 328). Table S4. Summary of blood pathogens detected in severe burn patients with bacteremia [a total of 102 positive blood cultures were reported in 91 patients, n(%)].
Mars Equation of anomaly at minimum distance, Toledan tables by Pedersen (Fritz S.) (2002) | Type-B (ID n° 302)
Creators: name: Pedersen, Fritz S.
Publisher: name: NAKALA - https://nakala.fr (Huma-Num - CNRS)
publisherIdentifier: https://ror.org/04ces3204
publisherIdentifierScheme: ROR
Year: 2022
Subjects: Astronomy
History of astronomy
DISHAS
Astronomical table

description: This is a type B edition: A type-B edition corresponds to a critical edition of several table witnesses:
 it is based on several different editions, themselves based on tables from different primary sources.
 A type-B edition can be based on type-A, type-B or type-C editions.
You can find it on the DISHAS project website from this link: https://dishas.obspm.fr/table-edition/302
description: Source: The Toledan Tables, Pedersen (Fritz S.), 2002
Flexible LED Index Modulation for MIMO Optical Wireless Communications
Creators: name: Yesilkaya, Anil
name: Purwita, Ardimas Andi
name: Panayirci, Erdal
name: Poor, H. Vincent
name: Haas, Harald
Publisher: name: arXiv
Year: 2022
Subjects: subject: Information Theory (cs.IT)
subject: Signal Processing (eess.SP)
subject: FOS: Computer and information sciences
subject: FOS: Electrical engineering, electronic engineering, information engineering

description: The limited bandwidth of optical wireless communication (OWC) front-end devices motivates the use of multiple-input-multiple-output (MIMO) techniques to enhance data rates. It is known that very high multiplexing gains could be achieved by spatial multiplexing (SMX) in exchange for exhaustive detection complexity. Alternatively, in spatial modulation (SM), a single light emitting diode (LED) is activated per time instance where information is carried by both the signal and the LED index. Since only an LED is active, both transmitter (TX) and receiver (RX) complexity reduces significantly while retaining the information transmission in the spatial domain. However, significant spectral efficiency losses occur in SM compared to SMX. In this paper, we propose a technique which adopts the advantages of both systems. Accordingly, the proposed flexible LED index modulation (FLIM) technique harnesses the inactive state of the LEDs as a transmit symbol. Therefore, the number of active LEDs changes in each transmission, unlike conventional techniques. Moreover, the system complexity is reduced by employing a linear minimum mean squared error (MMSE) equalizer and an angle perturbed receiver at the RX. Numerical results show that FLIM outperforms the reference systems by at least 6 dB in the low and medium/high spectral efficiency regions.
Lunar minutes of proportion, Toledan tables by Pedersen (Fritz S.) (2002) | Type-B (ID n° 287)
Creators: name: Pedersen, Fritz S.
Publisher: name: NAKALA - https://nakala.fr (Huma-Num - CNRS)
publisherIdentifier: https://ror.org/04ces3204
publisherIdentifierScheme: ROR
Year: 2022
Subjects: Astronomy
History of astronomy
DISHAS
Astronomical table

description: This is a type B edition: A type-B edition corresponds to a critical edition of several table witnesses:
 it is based on several different editions, themselves based on tables from different primary sources.
 A type-B edition can be based on type-A, type-B or type-C editions.
You can find it on the DISHAS project website from this link: https://dishas.obspm.fr/table-edition/287
description: Source: The Toledan Tables, Pedersen (Fritz S.), 2002
Character varieties and algebraic surfaces for the topology of quantum computing
Creators: name: Planat, Michel
name: Amaral, Marcelo M.
name: Fang, Fang
name: Chester, David
name: Aschheim, Raymond
name: Irwin, Klee
Publisher: name: arXiv
Year: 2022
Subjects: subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: It is shown that the representation theory of some finitely presented groups thanks to their $SL_2(\mathbb{C})$ character variety is related to algebraic surfaces. We make use of the Enriques-Kodaira classification of algebraic surfaces and the related topological tools to make such surfaces explicit. We study the connection of $SL_2(\mathbb{C})$ character varieties to topological quantum computing (TQC) as an alternative to the concept of anyons. The Hopf link $H$, whose character variety is a Del Pezzo surface $f_H$ (the trace of the commutator), is the kernel of our view of TQC. Qutrit and two-qubit magic state computing, derived from the trefoil knot in our previous work, may be seen as TQC from the Hopf link. The character variety of some two-generator Bianchi groups as well as that of the fundamental group for the singular fibers $\tilde{E}_6$ and $\tilde{D}_4$ contain $f_H$. A surface birationally equivalent to a $K_3$ surface is another compound of their character varieties.
description: 12 pages
Influence of C/O Ratio on Hot Jupiter Atmospheric Chemistry
Creators: name: Fleury, Benjamin
name: Gudipati, Murthy S.
name: Henderson, Bryana L.
name: Swain, Mark
Publisher: name: arXiv
Year: 2022
Subjects: subject: Earth and Planetary Astrophysics (astro-ph.EP)
subject: FOS: Physical sciences

description: We have conducted laboratory experiments to study the chemistry in hot Jupiter atmospheres with C/O ratio of 0.35. We have compared our results with the ones obtained previously for atmospheres with a C/O ratio of 1 to investigate the influence of the C/O ratio on the chemistry and formation of photochemical organic aerosol. We found that the C/O ratio and the gas mixture compositions strongly influence the pathways responsible for the formation of CO2. Thermochemical reactions are primarily responsible for the formation of CO2 in low C/O ratio atmospheres, while photochemistry is the dominant process in high C/O ratio atmospheres even if the final CO2 concentration is the same in both cases. Our results show that low C/O atmospheres at the thermochemical equilibrium contain a higher water abundance, while high C/O atmospheres are significantly depleted in water. However, in low C/O atmospheres, the water abundance is not affected by UV photolysis, while our previous work demonstrated that significant amount of water can be produced in high C/O ratio atmospheres. This contrast in water production suggests that photochemistry should be considered when interpreting exoplanet transit spectra. Finally, we did not observe the formation of a detectable amount of non-volatile photochemical aerosols in low C/O atmospheres, in contrast to our previous study. We infer that for C/O ratio < 1, water likely inhibits organic growth and aerosol formation, suggesting that photochemical organic aerosols are likely to be observed in planets presenting a carbon enrichment compared to their host stars.
description: 23 pages, 3 tables, 6 figures
Spatial Likelihood Voting with Self-Knowledge Distillation for Weakly Supervised Object Detection
Creators: name: Chen, Ze
name: Fu, Zhihang
name: Huang, Jianqiang
name: Tao, Mingyuan
name: Jiang, Rongxin
name: Tian, Xiang
name: Chen, Yaowu
name: Hua, Xian-sheng
Publisher: name: arXiv
Year: 2022
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Computer and information sciences

description: Weakly supervised object detection (WSOD), which is an effective way to train an object detection model using only image-level annotations, has attracted considerable attention from researchers. However, most of the existing methods, which are based on multiple instance learning (MIL), tend to localize instances to the discriminative parts of salient objects instead of the entire content of all objects. In this paper, we propose a WSOD framework called the Spatial Likelihood Voting with Self-knowledge Distillation Network (SLV-SD Net). In this framework, we introduce a spatial likelihood voting (SLV) module to converge region proposal localization without bounding box annotations. Specifically, in every iteration during training, all the region proposals in a given image act as voters voting for the likelihood of each category in the spatial dimensions. After dilating the alignment on the area with large likelihood values, the voting results are regularized as bounding boxes, which are then used for the final classification and localization. Based on SLV, we further propose a self-knowledge distillation (SD) module to refine the feature representations of the given image. The likelihood maps generated by the SLV module are used to supervise the feature learning of the backbone network, encouraging the network to attend to wider and more diverse areas of the image. Extensive experiments on the PASCAL VOC 2007/2012 and MS-COCO datasets demonstrate the excellent performance of SLV-SD Net. In addition, SLV-SD Net produces new state-of-the-art results on these benchmarks.
description: arXiv admin note: text overlap with arXiv:2006.12884
ИЗМЕНЕНИЯ В ПОЛОСТИ РТА И ПАРОДОНТЕ У ДЕТЕЙ С САХАРНЫМ ДИАБЕТОМ 1 ТИПА: КЛИНИЧЕСКИЕ И МОЛЕКУЛЯРНЫЕ АСПЕКТЫ.
Creators: name: Авазова, Ш.Н.
name: Ахмедова, С.М.
name: Усманова, Ш.Р.
Publisher: name: Zenodo
Year: 2024

description: Сахарный диабет 1 типа (СД1) у детей представляет собой серьезную проблему здравоохранения, требующую внимательного изучения его последствий для полости рта и пародонта [1]. В данном тезисе представлен обзор клинических и молекулярных изменений, происходящих в полости рта и пародонте у детей с СД1. Были проанализированы основные факторы, влияющие на развитие пародонтита и кариеса у детей с СД1, а также рассмотрены методы предупреждения и лечения этих заболеваний.
RankNEAT: Outperforming Stochastic Gradient Search in Preference Learning Tasks
Creators: name: Pinitas, Kosmas
name: Makantasis, Konstantinos
name: Liapis, Antonios
name: Yannakakis, Georgios N.
Publisher: name: arXiv
Year: 2022
Subjects: subject: Neural and Evolutionary Computing (cs.NE)
subject: Artificial Intelligence (cs.AI)
subject: Human-Computer Interaction (cs.HC)
subject: FOS: Computer and information sciences

description: Stochastic gradient descent (SGD) is a premium optimization method for training neural networks, especially for learning objectively defined labels such as image objects and events. When a neural network is instead faced with subjectively defined labels--such as human demonstrations or annotations--SGD may struggle to explore the deceptive and noisy loss landscapes caused by the inherent bias and subjectivity of humans. While neural networks are often trained via preference learning algorithms in an effort to eliminate such data noise, the de facto training methods rely on gradient descent. Motivated by the lack of empirical studies on the impact of evolutionary search to the training of preference learners, we introduce the RankNEAT algorithm which learns to rank through neuroevolution of augmenting topologies. We test the hypothesis that RankNEAT outperforms traditional gradient-based preference learning within the affective computing domain, in particular predicting annotated player arousal from the game footage of three dissimilar games. RankNEAT yields superior performances compared to the gradient-based preference learner (RankNet) in the majority of experiments since its architecture optimization capacity acts as an efficient feature selection mechanism, thereby, eliminating overfitting. Results suggest that RankNEAT is a viable and highly efficient evolutionary alternative to preference learning.
Entretien de Yazan Muhammad al-Aytan auprès de Farhan al-Adinan à propos de Bani Sakhar et de la Grande révolte arabe
Creators: name: al-Adinan, Farhan
name: Yazan Muhammad Al-Aytan (Enquêteur)
Publisher: name: NAKALA - https://nakala.fr (Huma-Num - CNRS)
publisherIdentifier: https://ror.org/04ces3204
publisherIdentifierScheme: ROR
Year: 2020

description: Source: http://www.calames.abes.fr/pub/ms/FileId-3139
description: Coverage: Amman, Jordanie
Practitioner Survey for Smart Car Parking
Creators: name: Ozkaya, Mert
Publisher: name: Zenodo
Year: 2024

description: In this survey, we aimed to understand practitioners' perspectives on smart car parking.

Our survey targets park controllers and includes 15 questions to understand their current challenges on the car parking processes and their expectations from a smart car parking system that optimises the car parking processes.

Our survey has been participated by the park controllers who work for ISPARK which is the subsidiary of the Istanbul Municipality. The park controllers do not currently use any software applications to manage car parking processes. Each park controller is responsible for one unique street They direct the drivers arriving to the empty parking spaces and process the drivers' payment.

We have managed to receive responses from 15 park controllers. Since the park controllers only speak Turkish, our survey here has been designed and executed in Turkish.
Procedural Content Generation using Neuroevolution and Novelty Search for Diverse Video Game Levels
Creators: name: Beukman, Michael
name: Cleghorn, Christopher W
name: James, Steven
Publisher: name: arXiv
Year: 2022
Subjects: subject: Neural and Evolutionary Computing (cs.NE)
subject: FOS: Computer and information sciences

description: Procedurally generated video game content has the potential to drastically reduce the content creation budget of game developers and large studios. However, adoption is hindered by limitations such as slow generation, as well as low quality and diversity of content. We introduce an evolutionary search-based approach for evolving level generators using novelty search to procedurally generate diverse levels in real time, without requiring training data or detailed domain-specific knowledge. We test our method on two domains, and our results show an order of magnitude speedup in generation time compared to existing methods while obtaining comparable metric scores. We further demonstrate the ability to generalise to arbitrary-sized levels without retraining.
description: Accepted to the Genetic and Evolutionary Computation Conference (GECCO '22), July 9--13, 2022, Boston, MA, USA. Code is located at https://github.com/Michael-Beukman/PCGNN
title: Korrektur der Ersten Ordnung zur Änderung des Besonderen Teils der Prüfungsordnung für den Masterstudiengang „Psychologie mit Schwerpunkt Arbeits- und Organisationspsychologie und Verkehrs- und Ingenieurpsychologie" der Fakultät für Lebenswissenschaften der Technischen Universität Braunschweig (Bek. v. 25.03.2024 mit TU-Verkündungsblatt Nr. 1555)
Creators: name: :None
Publisher: name: Technische Universität Braunschweig. Fakultät 2
Year: 2024
Subjects: subject: Prüfungsordnung
subject: 379

description: Hochschulöffentliche Bekanntmachungen der Technischen Universität Braunschweig, vol. 1555a
EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification
Creators: name: Salami, Abbas
name: Andreu-Perez, Javier
name: Gillmeister, Helge
Publisher: name: arXiv
Year: 2022
Subjects: subject: Machine Learning (cs.LG)
subject: Artificial Intelligence (cs.AI)
subject: Neurons and Cognition (q-bio.NC)
subject: FOS: Computer and information sciences
subject: FOS: Biological sciences

description: In recent years, neural networks and especially deep architectures have received substantial attention for EEG signal analysis in the field of brain-computer interfaces (BCIs). In this ongoing research area, the end-to-end models are more favoured than traditional approaches requiring signal transformation pre-classification. They can eliminate the need for prior information from experts and the extraction of handcrafted features. However, although several deep learning algorithms have been already proposed in the literature, achieving high accuracies for classifying motor movements or mental tasks, they often face a lack of interpretability and therefore are not quite favoured by the neuroscience community. The reasons behind this issue can be the high number of parameters and the sensitivity of deep neural networks to capture tiny yet unrelated discriminative features. We propose an end-to-end deep learning architecture called EEG-ITNet and a more comprehensible method to visualise the network learned patterns. Using inception modules and causal convolutions with dilation, our model can extract rich spectral, spatial, and temporal information from multi-channel EEG signals with less complexity (in terms of the number of trainable parameters) than other existing end-to-end architectures, such as EEG-Inception and EEG-TCNet. By an exhaustive evaluation on dataset 2a from BCI competition IV and OpenBMI motor imagery dataset, EEG-ITNet shows up to 5.9\% improvement in the classification accuracy in different scenarios with statistical significance compared to its competitors. We also comprehensively explain and support the validity of network illustration from a neuroscientific perspective. We have also made our code open at https://github.com/AbbasSalami/EEG-ITNet
BEHAVE: Dataset and Method for Tracking Human Object Interactions
Creators: name: Bhatnagar, Bharat Lal
name: Xie, Xianghui
name: Petrov, Ilya A.
name: Sminchisescu, Cristian
name: Theobalt, Christian
name: Pons-Moll, Gerard
Publisher: name: arXiv
Year: 2022
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Computer and information sciences

description: Modelling interactions between humans and objects in natural environments is central to many applications including gaming, virtual and mixed reality, as well as human behavior analysis and human-robot collaboration. This challenging operation scenario requires generalization to vast number of objects, scenes, and human actions. Unfortunately, there exist no such dataset. Moreover, this data needs to be acquired in diverse natural environments, which rules out 4D scanners and marker based capture systems. We present BEHAVE dataset, the first full body human- object interaction dataset with multi-view RGBD frames and corresponding 3D SMPL and object fits along with the annotated contacts between them. We record around 15k frames at 5 locations with 8 subjects performing a wide range of interactions with 20 common objects. We use this data to learn a model that can jointly track humans and objects in natural environments with an easy-to-use portable multi-camera setup. Our key insight is to predict correspondences from the human and the object to a statistical body model to obtain human-object contacts during interactions. Our approach can record and track not just the humans and objects but also their interactions, modeled as surface contacts, in 3D. Our code and data can be found at: http://virtualhumans.mpi-inf.mpg.de/behave
description: Accepted at CVPR'22
Additional file 1 of Hormone biosynthesis and metabolism members of 2OGD superfamily are involved in berry development and respond to MeJA and ABA treatment of Vitis vinifera L
Creators: name: Gao, Yingying
name: Wang, Xiaochen
name: Liu, Xianju
name: Liang, Zhenchang
Publisher: name: figshare
Year: 2022
Subjects: subject: Genetics
subject: FOS: Biological sciences

description: Additional file 1: Supplementary Figure S1. Evolutionary tree of 163 Vv2OGDs and 130 At2OGDs. The branches in different clades are distinguished in different color. DOXA, DOXB and DOXC class are indicated at the outer arc, and different clades are indicated at the inside arc. Supplementary Figure S2. Sequence alignment of 37 Vv2OGD-H proteins. VvLBO3 is too long (701aa) to display, it is cut partly in C terminal. The locations of His-Xaa-Asp/Glu-(Xaa)n-His [HX(D/E)XnH] and Arg-Xaa-Ser/Thr (RxS/T) motif are highlighted in red and orange boxes respectively. Supplementary Figure S3. Motif analysis of 31 At2OGD-H and 37 Vv2OGD-H members. Phylogenetic tree (left) of 31 At2OGD-Hs and 37 Vv2OGD-Hs and their motif analysis (right). Different motifs are represented by different colored block with numbers in it. Sequence of each motif is listed in Suppl. Table S3. Supplementary Table S1. Detailed information of 163 members of Vv2OGD superfamily. Supplementary Table S2. Primers used in this study. Supplementary Table S3. Sequence and details of each motif in Suppl. Fig. S3.
JUE Insight: The (Non-)Effect of Opportunity Zones on Housing Prices
Creators: name: Chen, Jiafeng
name: Glaeser, Edward
name: Wessel, David
Publisher: name: arXiv
Year: 2022
Subjects: subject: General Economics (econ.GN)
subject: FOS: Economics and business

description: Will the Opportunity Zones (OZ) program, America's largest new place-based policy in decades, generate neighborhood change? We compare single-family housing price growth in OZs with price growth in areas that were eligible but not included in the program. We also compare OZs to their nearest geographic neighbors. Our most credible estimates rule out price impacts greater than 0.5 percentage points with 95% confidence, suggesting that, so far, home buyers don't believe that this subsidy will generate major neighborhood change. OZ status reduces prices in areas with little employment, perhaps because buyers think that subsidizing new investment will increase housing supply. Mixed evidence suggests that OZs may have increased residential permitting.
description: To appear in Journal of Urban Economics
Impact of centre vortex removal on the Landau-gauge quark propagator in dynamical QCD
Creators: name: Virgili, Adam
name: Kamleh, Waseem
name: Leinweber, Derek
Publisher: name: arXiv
Year: 2022
Subjects: subject: High Energy Physics - Lattice (hep-lat)
subject: Nuclear Theory (nucl-th)
subject: FOS: Physical sciences

description: The important role of centre vortices in dynamical chiral symmetry breaking and corresponding dynamical mass generation has been demonstrated in pure gauge studies of the Landau-gauge quark propagator. We present initial results of our investigation into the impact of centre vortex removal on the Landau-gauge propagator on dynamical gauge fields. To ensure sensitivity to the topology of the gauge fields, the propagator is computed with overlap fermions. Upon removal of centre vortices we find that dynamical mass generation is suppressed. The quark renormalisation function remains remarkably flat except in the deep infrared where it exhibits significant suppression. These effects become more prominent at lighter quark masses.
description: 8 pages, 3 figures. Proceedings of the 38th International Symposium on Lattice Field Theory (Lattice 2021), 26th - 30th July 2021, Zoom/Gather@Massachusetts Institute of Technology
Interpretability of Machine Learning Methods Applied to Neuroimaging
Creators: name: Thibeau-Sutre, Elina
name: Collin, Sasha
name: Burgos, Ninon
name: Colliot, Olivier
Publisher: name: arXiv
Year: 2022
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: Artificial Intelligence (cs.AI)
subject: Machine Learning (cs.LG)
subject: Neurons and Cognition (q-bio.NC)
subject: Quantitative Methods (q-bio.QM)
subject: FOS: Computer and information sciences
subject: FOS: Biological sciences

description: Deep learning methods have become very popular for the processing of natural images, and were then successfully adapted to the neuroimaging field. As these methods are non-transparent, interpretability methods are needed to validate them and ensure their reliability. Indeed, it has been shown that deep learning models may obtain high performance even when using irrelevant features, by exploiting biases in the training set. Such undesirable situations can potentially be detected by using interpretability methods. Recently, many methods have been proposed to interpret neural networks. However, this domain is not mature yet. Machine learning users face two major issues when aiming to interpret their models: which method to choose, and how to assess its reliability? Here, we aim at providing answers to these questions by presenting the most common interpretability methods and metrics developed to assess their reliability, as well as their applications and benchmarks in the neuroimaging context. Note that this is not an exhaustive survey: we aimed to focus on the studies which we found to be the most representative and relevant.
Hate Speech Classification Using SVM and Naive BAYES
Creators: name: Asogwa, D. C
name: Chukwuneke, C. I
name: Ngene, C. C
name: Anigbogu, G. N
Publisher: name: arXiv
Year: 2022
Subjects: subject: Computation and Language (cs.CL)
subject: FOS: Computer and information sciences

description: The spread of hatred that was formerly limited to verbal communications has rapidly moved over the Internet. Social media and community forums that allow people to discuss and express their opinions are becoming platforms for the spreading of hate messages. Many countries have developed laws to avoid online hate speech. They hold the companies that run the social media responsible for their failure to eliminate hate speech. But as online content continues to grow, so does the spread of hate speech However, manual analysis of hate speech on online platforms is infeasible due to the huge amount of data as it is expensive and time consuming. Thus, it is important to automatically process the online user contents to detect and remove hate speech from online media. Many recent approaches suffer from interpretability problem which means that it can be difficult to understand why the systems make the decisions they do. Through this work, some solutions for the problem of automatic detection of hate messages were proposed using Support Vector Machine (SVM) and Naïve Bayes algorithms. This achieved near state-of-the-art performance while being simpler and producing more easily interpretable decisions than other methods. Empirical evaluation of this technique has resulted in a classification accuracy of approximately 99% and 50% for SVM and NB respectively over the test set. Keywords: classification; hate speech; feature extraction, algorithm, supervised learning
Reflective Fiber Faults Detection and Characterization Using Long-Short-Term Memory
Creators: name: Abdelli, Khouloud
name: Griesser, Helmut
name: Ehrle, Peter
name: Tropschug, Carsten
name: Pachnicke, Stephan
Publisher: name: arXiv
Year: 2022
Subjects: subject: Networking and Internet Architecture (cs.NI)
subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences

description: To reduce operation-and-maintenance expenses (OPEX) and to ensure optical network survivability, optical network operators need to detect and diagnose faults in a timely manner and with high accuracy. With the rapid advancement of telemetry technology and data analysis techniques, data-driven approaches leveraging telemetry data to tackle the fault diagnosis problem have been gaining popularity due to their quick implementation and deployment. In this paper, we propose a novel multi-task learning model based on long short-term memory (LSTM) to detect, locate, and estimate the reflectance of fiber reflective faults (events) including the connectors and the mechanical splices by extracting insights from monitored data obtained by the optical time domain reflectometry (OTDR) principle commonly used for troubleshooting of fiber optic cables or links. The experimental results prove that the proposed method: (i) achieves a good detection capability and high localization accuracy within short measurement time even for low SNR values; and (ii) outperforms conventionally employed techniques.
HTR and the crowd
Creators: name: van Oort, Thunnis
name: Tjong Kim Sang, Erik
name: Quanjer, Björn
name: Hoek, Lisa
name: van Galen, Coen
Publisher: name: Zenodo
Year: 2024
Subjects: HTR
crowd-sourcing
historical demographics

description: Slides for the DH Benelus presentation "HTR and the crowd", Friday 7 June 2024, 09:00-10:15, Session 6c